R Notebook
Height analysis
# install.packages("zoo")
library(dplyr)
library(ggplot2)
library(lubridate)
library(hydroGOF)
library(xtable)
library(knitr)
library(tidyr)
library(RSQLite)
library(agricolae)
library(scales)
library(zoo)
library(lme4)lode observed data
upDir <- "D:/R/CombinedData/"
obsData <- "D:/R/CombinedData/"
obsAll <- read.table(paste0(obsData, "ObsAll.txt"),
header = TRUE)
obsA<- obsAll %>%
mutate(StartDate=dmy(StartDate),MidDate=dmy(MidDate),FinishDate=dmy(FinishDate),Clock.Today=dmy(Clock.Today)) %>%
#mutate(SowingDate=as.factor(ifelse(SowingDate=="no","Sd_No",paste0("Sd_",SowingDate)))) %>% # assume this is typo to be fixed?
mutate(GrowthSeason1=as.factor(paste0("Gs_",GrowthSeason))) %>% # creating more intuitive labels here
mutate(Rotation1=as.factor(paste0("Rt_",Rotation))) %>%
mutate(ExpUnitCode=as.factor(paste0(Name,GrowthSeason1,Rotation1))) %>%
mutate(Clock.Today1 = as.POSIXct(paste(Clock.Today,Time),format="%Y-%m-%d %H:%M:%S"))
summary(obsA) Clock.Today Name Collection Experiment Water Defoliation
Min. :1997-01-28 Iversen_91DefoliationLL : 834 1997_2001: 961 Lincoln2010:2513 dry:3041 HH: 288
1st Qu.:2001-08-30 Iversen_121DefoliationLLFDFD5: 734 2000_2002:1070 Lincoln2000:1222 irr:3428 LL:6181
Median :2011-04-30 Iversen_8Waterirr : 500 2002_2004: 834 Lincoln2015:1089
Mean :2008-01-24 Iversen_8Waterdry : 461 2010_2012:2513 Lincoln2001: 387
3rd Qu.:2012-04-04 Iversen_12SowingDateSD1 : 425 2014_2018:1090 Lincoln2003: 365
Max. :2018-01-15 Iversen_12SowingDateSD2 : 390 2014_2019: 1 Lincoln2002: 247
(Other) :3125 (Other) : 646
SowingDate FD GrowthSeason Rotation StartDate MidDate FinishDate
No :1795 FD10: 35 Min. :1.00 Min. :1.000 Min. :1996-11-01 Min. :1996-12-27 Min. :1997-02-21
no :1091 FD2 : 34 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:2001-07-03 1st Qu.:2001-08-16 1st Qu.:2001-09-27
SD1 : 961 FD5 :6400 Median :2.00 Median :3.000 Median :2011-03-11 Median :2011-04-11 Median :2011-05-17
SD2 : 592 Mean :1.99 Mean :2.964 Mean :2007-12-17 Mean :2008-01-17 Mean :2008-02-18
SD3 : 545 3rd Qu.:2.00 3rd Qu.:4.000 3rd Qu.:2012-02-28 3rd Qu.:2012-04-02 3rd Qu.:2012-05-01
SD4 : 488 Max. :6.00 Max. :7.000 Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15
(Other): 997
Interval Variable VariableUnits Time Observed StdDEV GrowthSeason1
Min. : 0.00 NodeNumber :1297 % : 220 12:00:00:6469 Min. : 0.0 Min. : 0.0 Gs_1:2537
1st Qu.: 20.00 shootbiomass: 980 cm : 454 1st Qu.: 4.2 1st Qu.: 0.1 Gs_2:2690
Median : 33.00 LAI : 924 fractio0l:1958 Median : 13.7 Median : 0.6 Gs_3: 515
Mean : 37.96 Height : 697 Fraction : 434 Mean : 1019.3 Mean : 110.0 Gs_4: 276
3rd Qu.: 51.00 SWC : 575 kg/ha :1661 3rd Qu.: 694.8 3rd Qu.: 73.5 Gs_5: 400
Max. :125.00 TotalLeafNo : 382 m2/m2 : 924 Max. :16554.5 Max. :7307.6 Gs_6: 51
(Other) :1614 mm : 818 NA's :1192
Rotation1 ExpUnitCode Clock.Today1
Rt_1:1891 Iversen_91DefoliationLLGs_2Rt_1 : 80 Min. :1997-01-28 12:00:00
Rt_2:1303 Iversen_12SowingDateSD9Gs_1Rt_1 : 75 1st Qu.:2001-08-30 12:00:00
Rt_3:1032 Iversen_91DefoliationLLGs_1Rt_1 : 67 Median :2011-04-30 12:00:00
Rt_4: 745 Iversen_12SowingDateSD8Gs_1Rt_1 : 66 Mean :2008-01-24 23:25:59
Rt_5: 663 Iversen_12SowingDateSD6Gs_1Rt_1 : 64 3rd Qu.:2012-04-04 12:00:00
Rt_6: 558 Iversen_12SowingDateSD10Gs_1Rt_1: 63 Max. :2018-01-15 12:00:00
Rt_7: 277 (Other) :6054 obsALoad Tt and Join Observed data together
upDir <- "D:/R/"
obsData <- "D:/R/TtAll/"
Tt<- read.table(paste0(obsData, "df.all.txt"),
header = TRUE)
TtA <- Tt %>% mutate(Clock.Today=dmy(Clock.Today), ExpUnitCode=as.factor(ExpName))
TtAObsH <-merge(obsA,TtA,by=c("Clock.Today","ExpUnitCode")) %>%
mutate(GrowthRotation=as.factor(paste0(GrowthSeason.x,Rotation.x)))%>%
dplyr::filter(Water.x=="irr")%>%
dplyr::filter(Defoliation.x=="LL")%>%
dplyr::filter(Variable=="Height")%>%
dplyr::filter(Tb==1)
summary(ObsH) Clock.Today ExpUnitCode Name Collection
Min. :1997-10-23 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Iversen_121DefoliationLLFDFD5 :108 1997_2001: 68
1st Qu.:2001-02-20 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Iversen_91DefoliationLL :107 2000_2002:125
Median :2002-10-25 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Iversen_8Waterirr : 68 2002_2004:107
Mean :2005-09-17 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Iversen_9SowingDateSD1Waterirr: 67 2010_2012: 0
3rd Qu.:2015-02-09 Iversen_8WaterirrGs_5Rt_1 : 10 Iversen_9SowingDateSD2Waterirr: 25 2014_2018:108
Max. :2018-01-15 Iversen_91DefoliationLLGs_2Rt_1 : 10 Iversen_9SowingDateSD3Waterirr: 18 2014_2019: 0
(Other) :340 (Other) : 15
Experiment.x Water.x Defoliation.x SowingDate.x FD.x GrowthSeason.x Rotation.x StartDate
Lincoln2000:131 dry: 0 HH: 0 No :175 FD10: 0 Min. :1.000 Min. :1.000 Min. :1997-10-07
Lincoln2015:108 irr:408 LL:408 no :108 FD2 : 0 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2001-01-24
Lincoln2003: 54 SD1 : 67 FD5 :408 Median :2.000 Median :3.000 Median :2002-10-06
Lincoln2001: 35 SD2 : 25 Mean :2.105 Mean :3.098 Mean :2005-08-16
Lincoln2004: 31 SD3 : 18 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:2015-01-30
Lincoln2002: 22 SD4 : 15 Max. :5.000 Max. :7.000 Max. :2017-12-04
(Other) : 27 (Other): 0
MidDate FinishDate Interval Variable VariableUnits Time
Min. :1997-10-28 Min. :1997-11-19 Min. : 0.00 Height :408 % : 0 12:00:00:408
1st Qu.:2001-02-13 1st Qu.:2001-03-23 1st Qu.: 14.75 Branch : 0 cm :233
Median :2002-10-26 Median :2002-11-16 Median : 28.00 Fraction : 0 fractio0l: 0
Mean :2005-09-14 Mean :2005-10-14 Mean : 32.18 HardStemWt: 0 Fraction : 0
3rd Qu.:2015-02-19 3rd Qu.:2015-03-11 3rd Qu.: 41.00 LAI : 0 kg/ha : 0
Max. :2017-12-25 Max. :2018-01-15 Max. :116.00 LeafWt : 0 m2/m2 : 0
(Other) : 0 mm :175
Observed StdDEV GrowthSeason1 Rotation1 Clock.Today1 year day
Min. : 0.00 Min. : 0.000 Gs_1:160 Rt_1:112 Min. :1997-10-23 12:00:00 Min. :1997 Min. : 1.0
1st Qu.: 10.17 1st Qu.: 0.000 Gs_2:140 Rt_2: 80 1st Qu.:2001-02-20 12:00:00 1st Qu.:2001 1st Qu.: 63.0
Median : 35.62 Median : 3.107 Gs_3: 54 Rt_3: 59 Median :2002-10-25 12:00:00 Median :2002 Median :138.5
Mean :107.19 Mean :12.001 Gs_4: 13 Rt_4: 50 Mean :2005-09-17 22:25:44 Mean :2005 Mean :170.4
3rd Qu.:129.12 3rd Qu.:16.955 Gs_5: 41 Rt_5: 46 3rd Qu.:2015-02-10 06:00:00 3rd Qu.:2015 3rd Qu.:292.0
Max. :681.30 Max. :91.520 Gs_6: 0 Rt_6: 42 Max. :2018-01-15 12:00:00 Max. :2018 Max. :365.0
NA's :229 Rt_7: 19
rain maxt mint mean radn wind vp
Min. : 0.000 Min. : 7.90 Min. :-4.900 Min. : 2.55 Min. : 1.50 Min. :0.700 Min. : 5.10
1st Qu.: 0.000 1st Qu.:15.32 1st Qu.: 4.375 1st Qu.:10.40 1st Qu.: 9.70 1st Qu.:2.900 1st Qu.:10.00
Median : 0.000 Median :19.00 Median : 8.700 Median :13.20 Median :16.14 Median :3.900 Median :11.40
Mean : 0.876 Mean :19.17 Mean : 7.853 Mean :13.49 Mean :16.73 Mean :3.997 Mean :11.77
3rd Qu.: 0.000 3rd Qu.:22.23 3rd Qu.:11.300 3rd Qu.:16.50 3rd Qu.:22.62 3rd Qu.:4.900 3rd Qu.:13.72
Max. :31.800 Max. :33.80 Max. :20.600 Max. :26.20 Max. :33.40 Max. :9.300 Max. :22.00
Pp Tb TTbeta Tbb TTbroken TbF TTfick
Min. :10.02 Min. :1 Min. : 0.128 Min. :1 Min. : 1.841 Min. :1 Min. : 2.222
1st Qu.:12.21 1st Qu.:1 1st Qu.: 2.247 1st Qu.:1 1st Qu.: 6.822 1st Qu.:1 1st Qu.: 8.172
Median :14.27 Median :1 Median : 4.118 Median :1 Median : 9.015 Median :1 Median :10.646
Mean :13.93 Mean :1 Mean : 5.812 Mean :1 Mean : 9.535 Mean :1 Mean :10.878
3rd Qu.:15.82 3rd Qu.:1 3rd Qu.: 8.085 3rd Qu.:1 3rd Qu.:12.040 3rd Qu.:1 3rd Qu.:13.588
Max. :16.65 Max. :1 Max. :23.343 Max. :1 Max. :20.258 Max. :1 Max. :20.813
ExpName Experiment.y Water.y Defoliation.y SowingDate.y FD.y
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Lincoln1997: 68 dry: 0 HH: 0 No :175 FD10: 0
Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Lincoln2000:110 irr:408 LL:408 no :108 FD2 : 0
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Lincoln2001: 15 LS: 0 SD1 : 67 FD5 :408
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Lincoln2002:107 SL: 0 SD2 : 25
Iversen_8WaterirrGs_5Rt_1 : 10 Lincoln2010: 0 SS: 0 SD3 : 18
Iversen_91DefoliationLLGs_2Rt_1 : 10 Lincoln2015:108 SD4 : 15
(Other) :340 (Other): 0
GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean
Gs_1:160 Rt_1 :112 Min. : 0.2834 Min. : 4.022 Min. : 3.333 Min. :10.25 Min. : 7.024
Gs_2:140 Rt_2 : 80 1st Qu.: 60.8023 1st Qu.: 165.210 1st Qu.: 142.690 1st Qu.:11.99 1st Qu.:10.974
Gs_3: 54 Rt_3 : 59 Median :123.4401 Median : 299.224 Median : 258.979 Median :14.48 Median :13.963
Gs_4: 13 Rt_4 : 50 Mean :157.2934 Mean : 326.606 Mean : 283.618 Mean :13.84 Mean :13.226
Gs_5: 41 Rt_5 : 46 3rd Qu.:218.2569 3rd Qu.: 453.159 3rd Qu.: 395.238 3rd Qu.:15.99 3rd Qu.:15.771
Gs_6: 0 Rt_6 : 42 Max. :722.6708 Max. :1186.654 Max. :1051.876 Max. :16.55 Max. :19.327
(Other): 19
GrowthRotation
11 : 60
12 : 38
21 : 24
26 : 24
13 : 23
22 : 23
(Other):216 obsheight<-ObsH%>%
dplyr::filter(Name=="Iversen_8Waterirr")
obsheight%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+
geom_smooth(method = "lm", se = TRUE,linetype=1 , colour="black")+
facet_grid(GrowthSeason.x~Rotation.x)+ggtitle("Iversen_8Waterirr")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
Iversen_91DefoliationLL
obsheight1<-ObsH%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")
obsheight1%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_91DefoliationLL")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE,linetype=1 , colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAIversen_9SowingDateSD1Waterirr
obsheight3<-ObsH%>%
dplyr::filter(Name=="Iversen_9SowingDateSD1Waterirr")%>%
mutate(Observed=Observed*10)
obsheight3%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD1Waterirr")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAIversen_9SowingDateSD2Waterirr
obsheight1<-ObsH%>%
dplyr::filter(Name=="Iversen_9SowingDateSD2Waterirr")%>%
mutate(Observed=Observed*10)
obsheight1obsheight1%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD2Waterirr")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAIversen_9SowingDateSD3Waterirr
obsheight1<-ObsH%>%
dplyr::filter(Name=="Iversen_9SowingDateSD3Waterirr")%>%
mutate(Observed=Observed*10)
obsheight1obsheight1%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD3Waterirr")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAIversen_9SowingDateSD4Waterirr
obsheight1<-ObsH%>%
dplyr::filter(Name=="Iversen_9SowingDateSD4Waterirr")%>%
mutate(Observed=Observed*10)
obsheight1obsheight1%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_9SowingDateSD4Waterirr")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAIverson12DefoliationFD5
obsheight1<-ObsH%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
mutate(Observed=Observed*10)
obsheight1%>%
ggplot(aes(x=Tt_broken_sum, y=Observed), colour=factor(Name))+geom_point(size=2)+theme_bw()+xlab("Thermal time(°Cd)")+ylab("Plant height (mm)")+ggtitle("Iversen_121DefoliationLLFDFD5")+
facet_grid(GrowthSeason.x~Rotation.x)+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="black")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAconvert unit
calculate for heightchron
obsH2<-ObsH%>%
dplyr::filter(Collection!="1997_2001")%>%
dplyr::filter(Collection!="2002_2004")%>%
mutate(Observed=Observed*10)
obsH3<-ObsH%>%
dplyr::filter(Collection!="2000_2002")%>%
dplyr::filter(Collection!="2014_2018")
obsH3obsHN<-rbind(obsH2,obsH3)
obsHNobsSlope <- obsHN%>%
group_by(Name,GrowthSeason.x,Rotation.x,Collection,Tmean,Ppm,GrowthRotation) %>%
do(mod = lm(Tt_broken_sum~Observed,data=.)) %>%
mutate(slope = summary(mod)$coeff[2]) %>%
dplyr::select(-mod)
obsSlopeload Rotation and Growth season
phyll <- "D:\\R\\"
StartGrazing <- read.table(paste0(phyll, "ExperimentList.txt"),
header = TRUE)
StartGrazing1<-StartGrazing %>%
mutate(GrowthRotation= as.factor(paste0(GrowthSeason,Rotation)))
HchronPp<- merge(StartGrazing1,obsSlope,by=c("Name","Collection","GrowthRotation"))
HchronPp1<-HchronPp%>%
dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="57")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="36")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="14")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="37")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="12")%>%
dplyr::filter(Name!="Iversen_121DefoliationLLFDFD5"|GrowthRotation!="41")%>%
dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="26")%>%
dplyr::filter(Name!="Iversen_8Waterirr"|GrowthRotation!="32")%>%
dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="27")%>%
dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="17")%>%
dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="11")%>%
# # dplyr::filter(Name!="Iversen_91DefoliationLL"|GrowthRotation!="17")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="14")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="21")%>%
# # dplyr::filter(Name!="Iversen_9SowingDateSD1Waterirr"|GrowthRotation!="26")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD2Waterirr"|GrowthRotation!="13")
##dplyr::filter(Name!="Iversen_9SowingDateSD4Waterirr"|GrowthRotation!="13")%>%
#dplyr::filter(Stage!="Seedling")
HchronPp1%>%
ggplot(aes(x=Ppm, y=slope, colour=factor(Name),label=GrowthRotation))+geom_text()+theme_bw()+xlab("Mean photoperiod (h)")+ylab(" Heightchron (°Cd/mm)")+
geom_smooth(method = "lm", se = TRUE, formula=y ~ poly(x, 2, raw=TRUE), colour="darkgrey")+
facet_wrap(~Stage,ncol = 2)+theme(legend.title = element_blank())+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
HchronPpX<-HchronPp1$Ppm
Y<-HchronPp1$slope
Xsq<-X^2
Xcub<-X^3
plot(X,Y, pch=19)
model1<-lm(Y~X)
model2<-lm(Y~X+Xsq)
model3<-lm(Y~X+Xsq+Xcub)
mod_lm <-lm(Y~X*(X<14.2)+X*(X>=14.2),data=HchronPp)
anova(model1)Analysis of Variance Table
Response: Y
Df Sum Sq Mean Sq F value Pr(>F)
X 1 5.3993 5.3993 52.073 2.48e-09 ***
Residuals 51 5.2881 0.1037
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1summary(model1)
Call:
lm(formula = Y ~ X)
Residuals:
Min 1Q Median 3Q Max
-0.42533 -0.19184 -0.09654 0.07362 0.92300
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.56353 0.36014 9.895 1.85e-13 ***
X -0.17883 0.02478 -7.216 2.48e-09 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.322 on 51 degrees of freedom
Multiple R-squared: 0.5052, Adjusted R-squared: 0.4955
F-statistic: 52.07 on 1 and 51 DF, p-value: 2.48e-09anova(model2)Analysis of Variance Table
Response: Y
Df Sum Sq Mean Sq F value Pr(>F)
X 1 5.3993 5.3993 67.150 8.334e-11 ***
Xsq 1 1.2677 1.2677 15.766 0.0002297 ***
Residuals 50 4.0204 0.0804
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1summary(model2)
Call:
lm(formula = Y ~ X + Xsq)
Residuals:
Min 1Q Median 3Q Max
-0.32871 -0.19718 -0.06290 0.04647 0.82851
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.33706 2.98204 5.143 4.53e-06 ***
X -1.89493 0.43275 -4.379 6.09e-05 ***
Xsq 0.06145 0.01547 3.971 0.00023 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.2836 on 50 degrees of freedom
Multiple R-squared: 0.6238, Adjusted R-squared: 0.6088
F-statistic: 41.46 on 2 and 50 DF, p-value: 2.426e-11anova(model3)Analysis of Variance Table
Response: Y
Df Sum Sq Mean Sq F value Pr(>F)
X 1 5.3993 5.3993 72.2337 3.33e-11 ***
Xsq 1 1.2677 1.2677 16.9599 0.0001462 ***
Xcub 1 0.3577 0.3577 4.7856 0.0334994 *
Residuals 49 3.6627 0.0747
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1summary(model3)
Call:
lm(formula = Y ~ X + Xsq + Xcub)
Residuals:
Min 1Q Median 3Q Max
-0.35197 -0.15505 -0.05562 0.04544 0.83225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.6600 26.8153 2.747 0.00839 **
X -14.7186 5.8768 -2.505 0.01564 *
Xsq 0.9921 0.4257 2.331 0.02393 *
Xcub -0.0223 0.0102 -2.188 0.03350 *
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.2734 on 49 degrees of freedom
Multiple R-squared: 0.6573, Adjusted R-squared: 0.6363
F-statistic: 31.33 on 3 and 49 DF, p-value: 1.873e-11abline(model1, col="red")XV<-seq(min(X),max(X),0.01)
yv<-predict(model2,list(X=XV,Xsq=XV^2))
lines(XV,yv,col="blue")
NA
NAFit a polynomial regression model
```{r}
HchronPp1
X<-HchronPp1\(Pmean # Y<-HchronPp1\)slope
Xsq<-X^2
Xcub<-X^3
plot(X,Y, pch=19)
model1<-lm(Y~X)
model2<-lm(Y~X+Xsq)
model3<-lm(Y~X+Xsq+Xcub)
mod_lm <-lm(Y~X(X<14.2)+X(X>=14.2),data=HchronPp1)
anova(model1)
summary(model1)
anova(model2)
summary(model2)
anova(model3)
summary(model3)
abline(model1, col=“red”)
XV<-seq(min(HchronPp1\(Pmean),max(HchronPp1\)Pmean),0.01)
yv<-predict(model2,list(X=XV,Xsq=XV^2))
lines(XV,yv,col=“blue”)
title(xlab=“Mean photoperiod (h)”,ylab=“Heightchron (mm/cd )”)
Define stats function
- Using Gauch et al. 2003 (Model evaluation by comparison of model-based predictions and measured values. Agron. J. 95, 1442-1446)
Test stats functions used
s <- c(4231.972,3935.604,3779.652,3627.687,3363.499,3230.566,2868.114,2868.827)
m <- c(4987.66,5636.09,4754.06,4114.53,4141.72,3704.06,5142.19,4762.03)
x <- gauchStats(s,m)
tempDf <- data.frame(statName=c("SB","NU","LC","r_MSD","R2"), statValue=x)
# kable(tempDf, digits= 2)
tempDf2 <- data.frame(Predicted=s, Observed=m)
x <- tempDf2 %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),1),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) %>%
t()
df <- data.frame(stat = row.names(x),statvalue = x[,1])
df %>%
kable(format = "markdown")| stat | statvalue | |
|---|---|---|
| n | n | 8.0 |
| r2 | r2 | 7.1 |
| r_rmse | r_rmse | 28.7 |
| nse | nse | -4.1 |
| sb | sb | 76.5 |
| nu | nu | 5.2 |
| lc | lc | 18.2 |
Load simulated database
create function to read data (Justin’s script)
load address of db
set table to be enquierd
load table into an object
make it a dataframe
change date to corerct format
explore the df
db.address <- "D:\\APSIMX2\\Prototypes\\Lucerne\\LucerneValidation.db"
tableName<-"Report"
DbTable <- GetApsimNGTable(db.address,tableName)
df <- as.data.frame(DbTable)
df$Clock.Today <- ymd_hms(df$Clock.Today)
str(df)'data.frame': 56703 obs. of 234 variables:
$ SimulationID : int 1 2 1 2 1 2 1 2 1 2 ...
$ Water : chr "dry" "irr" "dry" "irr" ...
$ Zone : chr "paddock" "paddock" "paddock" "paddock" ...
$ Clock.Today : POSIXct, format: "1996-10-31 12:00:00" "1996-10-31 12:00:00" "1996-11-01 12:00:00" "1996-11-01 12:00:00" ...
$ DiagnosticsVariables.Script.AccumPlantN : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumMineralisation : num 0 0 -0.00561 -0.00561 -0.00699 ...
$ DiagnosticsVariables.Script.AccumDenit : num 0 0 -0.184 -0.184 -0.254 ...
$ DiagnosticsVariables.Script.AccumFert : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumLeach : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumDetach : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.DeltaSoilOMN : num 0 0 0.00561 0.00561 0.00699 ...
$ DiagnosticsVariables.Script.DeltaSurfaceOMN : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.DeltaSoilMineralN : num 0 0 -0.189 -0.189 -0.261 ...
$ Lucerne.Root.NSupply.Fixation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Reallocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Retranslocation : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.NSupply.Uptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ Soil.SoilWater.Eo : num 1.89 1.89 4.13 4.13 4.58 ...
$ Soil.SoilWater.Es : num 1.89 1.89 4.13 4.13 3.74 ...
$ SWC : num 752 752 749 749 746 ...
$ DiagnosticsVariables.Script.DUL : num 744 744 744 744 744 744 744 744 744 744 ...
$ Soil.SoilWater.Drainage : num 0 0 0 0 0 0 0 0 0 0 ...
$ Soil.SoilWater.Runoff : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.OutFlowLat : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumEO : num -1.89 -1.89 -6.02 -6.02 -10.6 ...
$ DiagnosticsVariables.Script.AccumEP : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumES : num -1.89 -1.89 -6.02 -6.02 -9.76 ...
$ DiagnosticsVariables.Script.AccumDrainage : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumRunoff : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumRainfall : num 10.4 10.4 12.2 12.2 12.9 ...
$ DiagnosticsVariables.Script.AccumIrrigation : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.AccumOutflowLat : num 0 0 0 0 0 0 0 0 0 0 ...
$ DiagnosticsVariables.Script.SoilWaterDeficit : num 0 0 7.63 7.63 5.3 ...
$ Lucerne.Grain.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ StemWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Stem.Live.Wt : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Grain.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Shell.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ LeafWt : num 0 0 0 0 0 0 0 0 0 0 ...
$ RootWt : num 900 900 900 900 900 900 900 900 900 900 ...
$ Lucerne.Leaf.Live.N : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.N : num 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 ...
$ Lucerne.Leaf.Live.NConc : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Root.Live.NConc : num 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 ...
$ Lucerne.Root.WaterUptake : num 0 0 0 0 0 0 0 0 0 0 ...
$ ET : num 1.89 1.89 4.13 4.13 3.74 ...
$ Lucerne.Root.Depth : num 20 20 23 23 26 26 29 29 32 32 ...
$ Lucerne.Leaf.CoverTotal : num 0 0 0 0 0 0 0 0 0 0 ...
$ Lucerne.Leaf.CoverDead : num 0 0 0 0 0 0 0 0 0 0 ...
$ LAI : num 0 0 0 0 0 0 0 0 0 0 ...
$ Height : num 0 0 0 0 0 0 0 0 0 0 ...
$ SWmm.1. : num 35 35 33.6 33.6 32.1 ...
$ SWmm.2. : num 35.8 35.8 33.8 33.8 32.5 ...
$ SWmm.3. : num 35.1 35.1 33.9 33.9 32.7 ...
$ SWmm.4. : num 30.3 30.3 30.3 30.3 30.1 ...
$ SWmm.5. : num 28.4 28.4 28.4 28.4 28.2 ...
$ SWmm.6. : num 30.8 30.8 30.8 30.8 30.8 ...
$ SWmm.7. : num 32.4 32.4 32.4 32.4 32.4 ...
$ SWmm.8. : num 33.4 33.4 33.4 33.4 33.4 ...
$ SWmm.9. : num 33.6 33.6 33.6 33.6 33.6 ...
$ SWmm.10. : num 34.5 34.5 34.5 34.5 34.5 ...
$ SWmm.11. : num 34.5 34.5 34.5 34.5 34.5 ...
$ SWmm.12. : num 34.5 34.5 34.5 34.5 34.5 ...
$ SWmm.13. : num 34.5 34.5 34.5 34.5 34.5 ...
$ SWmm.14. : num 33.9 33.9 33.9 33.9 33.9 ...
$ SWmm.15. : num 33.6 33.6 33.9 33.9 33.9 ...
$ SWmm.16. : num 32.7 32.7 34 34 34 ...
$ SWmm.17. : num 30 30 30.6 30.6 32.1 ...
$ SWmm.18. : num 30 30 30 30 30 ...
$ SWmm.19. : num 30 30 30 30 30 30 30 30 30 30 ...
$ SWmm.20. : num 30.9 30.9 30.9 30.9 30.9 ...
$ SWmm.21. : num 31 31 31 31 31 31 31 31 31 31 ...
$ SWmm.22. : num 32 32 32 32 32 32 32 32 32 32 ...
$ SWmm.23. : num 34.9 34.9 34.9 34.9 34.9 ...
$ Soil.NO3N.1. : num 175 175 173 173 175 ...
$ Soil.NO3N.2. : num 47.2 47.2 47.8 47.8 45.7 ...
$ Soil.NO3N.3. : num 22.2 22.2 22.1 22.1 21.3 ...
$ Soil.NO3N.4. : num 4.05 4.05 5.12 5.12 5.53 ...
$ Soil.NO3N.5. : num 1.95 1.95 2.15 2.15 2.27 ...
$ Soil.NO3N.6. : num 1.67 1.67 1.71 1.71 1.74 ...
$ Soil.NO3N.7. : num 1.64 1.64 1.64 1.64 1.64 ...
$ Soil.NO3N.8. : num 1.6 1.6 1.6 1.6 1.6 ...
$ Soil.NO3N.9. : num 1.64 1.64 1.63 1.63 1.63 ...
$ Soil.NO3N.10. : num 0.187 0.187 0.274 0.274 0.326 ...
$ Soil.NO3N.11. : num 0.0161 0.0161 0.0291 0.0291 0.0378 ...
$ Soil.NO3N.12. : num 0.000302 0.000302 0.0003 0.0003 0.000319 ...
$ Soil.NO3N.13. : num 0.000144 0.000144 0.000278 0.000278 0.000308 ...
$ Soil.NO3N.14. : num 2.25e-05 2.25e-05 4.98e-05 4.98e-05 7.36e-05 ...
$ Soil.NO3N.15. : num 1.50e-05 1.50e-05 2.88e-05 2.88e-05 4.30e-05 ...
$ Soil.NO3N.16. : num 1.6 1.6 1.56 1.56 1.49 ...
$ Soil.NO3N.17. : num 1.6 1.6 1.65 1.65 1.72 ...
$ Soil.NO3N.18. : num 1.6 1.6 1.62 1.62 1.63 ...
$ Soil.NO3N.19. : num 1.61 1.61 1.62 1.62 1.63 ...
$ Soil.NO3N.20. : num 1.6 1.6 1.62 1.62 1.63 ...
$ Soil.NO3N.21. : num 1.61 1.61 1.62 1.62 1.63 ...
$ Soil.NO3N.22. : num 1.61 1.61 1.62 1.62 1.63 ...
$ Soil.NO3N.23. : num 1.6 1.6 1.61 1.61 1.63 ...
$ DiagnosticsVariables.Script.SoilNitrogenContent : num 272 272 272 272 272 ...
[list output truncated]summary(df) SimulationID Water Zone Clock.Today
Min. : 1.00 Length:56703 Length:56703 Min. :1979-01-01 12:00:00
1st Qu.:14.00 Class :character Class :character 1st Qu.:1998-11-20 12:00:00
Median :40.00 Mode :character Mode :character Median :2001-11-30 12:00:00
Mean :34.87 Mean :2004-04-22 12:38:26
DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
Min. : 0.0 Min. : -0.1345
1st Qu.:113.7 1st Qu.: 48.0568
Median :278.8 Median : 97.2750
Mean :253.9 Mean :132.5251
DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert DiagnosticsVariables.Script.AccumLeach
Min. :-120.284 Min. :0 Min. :-127.0290
1st Qu.: -11.333 1st Qu.:0 1st Qu.: -2.3541
Median : -3.323 Median :0 Median : -0.1743
Mean : -11.726 Mean :0 Mean : -4.5402
DiagnosticsVariables.Script.AccumDetach DiagnosticsVariables.Script.DeltaSoilOMN
Min. :0 Min. :-12180.53
1st Qu.:0 1st Qu.: -200.92
Median :0 Median : -91.00
Mean :0 Mean : -327.08
DiagnosticsVariables.Script.DeltaSurfaceOMN DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation
Min. :-16.0000 Min. :-258.73 Min. :0
1st Qu.: 0.0000 1st Qu.:-250.13 1st Qu.:0
Median : 0.0000 Median :-170.57 Median :0
Mean : -0.5151 Mean :-146.22 Mean :0
Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo
Min. :0 Min. :0 Min. :0 Min. : 0.05379
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.: 1.54350
Median :0 Median :0 Median :0 Median : 2.86545
Mean :0 Mean :0 Mean :0 Mean : 3.24309
Soil.SoilWater.Es SWC DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage Soil.SoilWater.Runoff
Min. :0.0000 Min. : 82.53 Min. : 103.0 Min. : 0.0000 Min. : 0.00000
1st Qu.:0.3311 1st Qu.: 480.80 1st Qu.: 723.0 1st Qu.: 0.0000 1st Qu.: 0.00000
Median :0.5887 Median : 657.43 Median : 744.0 Median : 0.0000 Median : 0.00000
Mean :0.8110 Mean : 661.72 Mean : 815.2 Mean : 0.1053 Mean : 0.01482
DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO DiagnosticsVariables.Script.AccumEP
Min. :0 Min. :-9185.882 Min. :-2437.1
1st Qu.:0 1st Qu.:-2923.566 1st Qu.: -971.0
Median :0 Median :-1652.845 Median : -504.9
Mean :0 Mean :-2071.465 Mean : -604.6
DiagnosticsVariables.Script.AccumES DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
Min. :-2096.227 Min. :-592.411 Min. :-217.719
1st Qu.: -755.592 1st Qu.: -57.094 1st Qu.: -8.244
Median : -425.968 Median : -7.275 Median : 0.000
Mean : -519.739 Mean : -65.213 Mean : -12.639
DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
Min. : 0.0 Min. :0
1st Qu.: 278.5 1st Qu.:0
Median : 734.4 Median :0
Mean : 911.5 Mean :0
DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
Min. :0 Min. :-624.63 Min. : 0.000
1st Qu.:0 1st Qu.:-228.45 1st Qu.: 0.000
Median :0 Median :-112.06 Median : 0.000
Mean :0 Mean :-153.36 Mean : 2.965
Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N LeafWt
Min. : 0.000 Min. : 0 Min. : 0.0 Min. :0.00000 Min. :0.00000 Min. : 0.0
1st Qu.: 0.000 1st Qu.: 567 1st Qu.: 56.7 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 415.2
Median : 0.000 Median : 1467 Median : 146.7 Median :0.00000 Median :0.00000 Median : 854.4
Mean : 2.965 Mean : 2336 Mean : 233.6 Mean :0.08896 Mean :0.08896 Mean :1219.2
RootWt Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
Min. : 0.0 Min. :0.000e+00 Min. :0.0000 Min. :0.00e+00 Min. :0.000000
1st Qu.: 149.4 1st Qu.:0.000e+00 1st Qu.:0.1472 1st Qu.:0.00e+00 1st Qu.:0.009827
Median : 275.3 Median :1.020e-08 Median :0.2709 Median :0.00e+00 Median :0.009935
Mean : 412.0 Mean :1.012e-04 Mean :0.4081 Mean :1.59e-05 Mean :0.009691
Lucerne.Root.WaterUptake ET Lucerne.Root.Depth Lucerne.Leaf.CoverTotal Lucerne.Leaf.CoverDead
Min. :0.00000 Min. : 0.0000 Min. : 0 Min. :0.00000 Min. :0
1st Qu.:0.05364 1st Qu.: 0.6631 1st Qu.:1500 1st Qu.:0.04093 1st Qu.:0
Median :0.42055 Median : 1.3066 Median :2300 Median :0.55514 Median :0
Mean :1.00188 Mean : 1.8129 Mean :1992 Mean :0.55734 Mean :0
LAI Height SWmm.1. SWmm.2. SWmm.3. SWmm.4. SWmm.5.
Min. :0.00000 Min. : 0.00 Min. : 1.00 Min. : 2.00 Min. : 5.00 Min. : 7.826 Min. : 7.389
1st Qu.:0.05159 1st Qu.: 82.63 1st Qu.:20.07 1st Qu.: 22.11 1st Qu.: 23.12 1st Qu.: 23.590 1st Qu.: 25.299
Median :1.00000 Median : 222.92 Median :29.36 Median : 32.63 Median : 29.89 Median : 28.929 Median : 27.807
Mean :2.29835 Mean : 314.30 Mean :29.57 Mean : 32.36 Mean : 36.09 Mean : 41.060 Mean : 43.080
SWmm.6. SWmm.7. SWmm.8. SWmm.9. SWmm.10. SWmm.11.
Min. : 7.291 Min. : 7.227 Min. : 7.33 Min. : 7.222 Min. : 7.313 Min. : 7.59
1st Qu.: 25.611 1st Qu.: 24.288 1st Qu.: 22.89 1st Qu.: 22.218 1st Qu.: 20.757 1st Qu.: 19.73
Median : 29.683 Median : 31.479 Median : 30.82 Median : 31.464 Median : 31.341 Median : 31.47
Mean : 44.685 Mean : 46.412 Mean : 45.17 Mean : 44.535 Mean : 44.817 Mean : 39.12
SWmm.12. SWmm.13. SWmm.14. SWmm.15. SWmm.16. SWmm.17. SWmm.18.
Min. : 8.58 Min. : 8.68 Min. : 9.10 Min. :10.00 Min. :10.23 Min. : 9.83 Min. : 9.78
1st Qu.: 20.67 1st Qu.:20.06 1st Qu.:19.55 1st Qu.:20.23 1st Qu.:21.79 1st Qu.:25.25 1st Qu.:25.24
Median : 29.86 Median :28.00 Median :29.81 Median :30.92 Median :30.76 Median :29.39 Median :29.97
Mean : 31.15 Mean :29.64 Mean :29.97 Mean :30.64 Mean :31.60 Mean :29.77 Mean :30.01
SWmm.19. SWmm.20. SWmm.21. SWmm.22. SWmm.23. Soil.NO3N.1. Soil.NO3N.2.
Min. : 9.72 Min. : 9.99 Min. :10.18 Min. :10.69 Min. :15.03 Min. : 0.000 Min. : 0.1319
1st Qu.:25.26 1st Qu.:25.34 1st Qu.:23.55 1st Qu.:18.39 1st Qu.:21.84 1st Qu.: 1.431 1st Qu.: 1.5505
Median :29.94 Median :30.55 Median :30.63 Median :30.80 Median :33.15 Median : 2.600 Median : 2.4889
Mean :30.22 Mean :30.52 Mean :30.61 Mean :29.32 Mean :31.69 Mean : 13.187 Mean : 6.3640
Soil.NO3N.3. Soil.NO3N.4. Soil.NO3N.5. Soil.NO3N.6. Soil.NO3N.7. Soil.NO3N.8. Soil.NO3N.9.
Min. : 0.2321 Min. : 0.2857 Min. : 0.3562 Min. : 0.486 Min. : 0.426 Min. : 0.432 Min. : 0.281
1st Qu.: 1.5254 1st Qu.: 1.2642 1st Qu.: 1.2458 1st Qu.: 1.279 1st Qu.: 1.042 1st Qu.: 1.007 1st Qu.: 0.934
Median : 2.4029 Median : 1.9555 Median : 1.7426 Median : 1.760 Median : 1.499 Median : 1.364 Median : 1.223
Mean : 5.8377 Mean : 4.5441 Mean : 3.8181 Mean : 3.104 Mean : 2.523 Mean : 1.821 Mean : 1.675
Soil.NO3N.10. Soil.NO3N.11. Soil.NO3N.12. Soil.NO3N.13. Soil.NO3N.14. Soil.NO3N.15. Soil.NO3N.16.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.058
1st Qu.: 0.609 1st Qu.: 0.468 1st Qu.: 0.334 1st Qu.: 0.255 1st Qu.: 0.224 1st Qu.: 0.233 1st Qu.: 0.442
Median : 0.837 Median : 0.708 Median : 0.584 Median : 0.531 Median : 0.506 Median : 0.473 Median : 0.515
Mean : 1.316 Mean : 1.388 Mean : 1.161 Mean : 1.136 Mean : 1.101 Mean : 1.073 Mean : 1.220
Soil.NO3N.17. Soil.NO3N.18. Soil.NO3N.19. Soil.NO3N.20. Soil.NO3N.21. Soil.NO3N.22. Soil.NO3N.23.
Min. :0.149 Min. :0.098 Min. :0.074 Min. :0.095 Min. :0.098 Min. :0.097 Min. :0.140
1st Qu.:0.373 1st Qu.:0.345 1st Qu.:0.320 1st Qu.:0.310 1st Qu.:0.315 1st Qu.:0.335 1st Qu.:0.379
Median :0.468 Median :0.435 Median :0.417 Median :0.405 Median :0.412 Median :0.464 Median :0.478
Mean :0.779 Mean :0.738 Mean :0.739 Mean :0.751 Mean :0.796 Mean :0.821 Mean :0.806
DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand DiagnosticsVariables.Script.FomN
Min. : 0.00 Min. :0.00000 Min. : 0.0000
1st Qu.: 20.47 1st Qu.:0.01241 1st Qu.: 0.3537
Median : 26.75 Median :0.08534 Median : 1.0975
Mean : 54.01 Mean :0.18648 Mean : 3.3132
DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN DiagnosticsVariables.Script.DltNMinRes
Min. : 0 Min. : 0.00 Min. :-1.679280
1st Qu.:11015 1st Qu.: 87.43 1st Qu.: 0.000000
Median :22728 Median :128.38 Median : 0.000000
Mean :17206 Mean :126.37 Mean :-0.001052
DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw Lucerne.Leaf.Fn Lucerne.Phenology.CurrentPhaseName
Min. :-0.07311 Min. :0.0000 Min. :0.0000000 Length:56703
1st Qu.: 0.07441 1st Qu.:1.0000 1st Qu.:0.0000000 Class :character
Median : 0.15414 Median :1.0000 Median :0.0000001 Mode :character
Mean : 0.19326 Mean :0.8591 Mean :0.0532373
Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass
Length:56703 Min. :1.000 Min. : 0.000 Min. : 0.0000 Min. : 0
Class :character 1st Qu.:4.310 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 1008
Mode :character Median :4.808 Median : 0.000 Median : 0.0000 Median : 2316
Mean :4.939 Mean : 5.931 Mean : 0.1779 Mean : 3614
Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4.
Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.000561 1st Qu.:0.0004222 1st Qu.:0.0002453 1st Qu.:0.0001412
Median :0.001456 Median :0.0010220 Median :0.0006460 Median :0.0004311
Mean :0.003661 Mean :0.0016247 Mean :0.0010904 Mean :0.0007684
Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
Min. :0.000e+00 Min. :0.0000 Min. :0.0000 Min. :0.000
1st Qu.:5.606e-05 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
Median :3.537e-04 Median :0.0003 Median :0.0003 Median :0.000
Mean :6.366e-04 Mean :0.0006 Mean :0.0006 Mean :0.001
Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.001 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0.763
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1500.000
Median :0.000 Median :0.000 Median :0.000 Median :2300.000
Mean :0.000 Mean :0.000 Mean :0.000 Mean :2043.145
Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1. Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3.
Min. : 0.0 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.00
1st Qu.: 100.8 1st Qu.: 1.709 1st Qu.: 6.85 1st Qu.: 8.134 1st Qu.: 6.00
Median : 231.6 Median : 4.422 Median :16.71 Median :19.774 Median :17.85
Mean : 361.4 Mean : 7.140 Mean :15.63 Mean :17.125 Mean :16.00
Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6. Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.: 3.823 1st Qu.: 2.194 1st Qu.: 1.203 1st Qu.: 2.039 1st Qu.: 3.33
Median :16.289 Median : 14.646 Median :15.028 Median :14.378 Median :14.72
Mean :14.744 Mean : 13.601 Mean :14.014 Mean :13.349 Mean :14.69
Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10. Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 2.195 1st Qu.: 1.617 1st Qu.: 2.878 1st Qu.: 8.027 1st Qu.: 7.445
Median :13.891 Median :13.651 Median :15.098 Median :16.565 Median :15.733
Mean :13.528 Mean :13.655 Mean :14.100 Mean :15.615 Mean :14.553
Soil.SoilWater.ESW.14. Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 7.172 1st Qu.: 8.141 1st Qu.:11.02 1st Qu.:14.00 1st Qu.:14.00
Median :16.238 Median :16.571 Median :16.94 Median :18.69 Median :18.63
Mean :14.820 Mean :15.538 Mean :15.72 Mean :16.90 Mean :17.14
Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22. Soil.SoilWater.ESW.23.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 6.025
1st Qu.:14.00 1st Qu.:15.72 1st Qu.:14.02 1st Qu.: 8.32 1st Qu.:12.608
Median :18.90 Median :20.30 Median :18.98 Median :18.16 Median :21.281
Mean :17.36 Mean :18.30 Mean :17.57 Mean :16.26 Mean :19.715
CheckpointID SowingDate Defoliation FD Factors Soil.OutputLayers.SWmm.1.
Min. :1 Length:56703 Length:56703 Length:56703 Length:56703 Min. : 3.00
1st Qu.:1 Class :character Class :character Class :character Class :character 1st Qu.: 14.34
Median :1 Mode :character Mode :character Mode :character Mode :character Median : 21.67
Mean :1 Mean : 25.46
Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
Min. : 21.00 Min. : 42.09 Min. : 0.00 Min. : 51.35
1st Qu.: 29.67 1st Qu.: 55.27 1st Qu.: 52.00 1st Qu.: 86.27
Median : 63.91 Median : 71.42 Median : 80.01 Median :119.08
Mean : 76.81 Mean : 87.61 Mean : 87.10 Mean :117.11
Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
Min. : 54.79 Min. : 57.97 Min. : 60.89 Min. :0.03
1st Qu.: 74.11 1st Qu.: 90.00 1st Qu.: 88.00 1st Qu.:0.07
Median : 93.97 Median : 99.01 Median : 92.08 Median :0.18
Mean :105.21 Mean :108.70 Mean :137.56 Mean :0.19
Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6.
Min. :0.05 Min. :0.05 Min. :0.00 Min. :0.22 Min. :0.19
1st Qu.:0.10 1st Qu.:0.16 1st Qu.:0.20 1st Qu.:0.24 1st Qu.:0.21
Median :0.23 Median :0.24 Median :0.25 Median :0.29 Median :0.28
Mean :0.23 Mean :0.24 Mean :0.25 Mean :0.30 Mean :0.29
Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10.
Min. :0.22 Min. :0.22 Min. :63.01 Min. :64.02
1st Qu.:0.23 1st Qu.:0.22 1st Qu.:63.35 1st Qu.:64.34
Median :0.29 Median :0.31 Median :63.71 Median :64.77
Mean :0.30 Mean :0.30 Mean :63.67 Mean :64.87
Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
Min. :64.00 Min. :60.11 Min. :58.00 Min. :54.00
1st Qu.:64.00 1st Qu.:60.19 1st Qu.:58.00 1st Qu.:54.02
Median :64.00 Median :60.36 Median :58.00 Median :54.20
Mean :64.36 Mean :60.74 Mean :58.00 Mean :54.24
Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17. Soil.OutputLayers.SWmm.18.
Min. :54.00 Min. :54.00 Min. :54.53 Min. :58.01
1st Qu.:54.02 1st Qu.:54.38 1st Qu.:54.68 1st Qu.:58.85
Median :54.08 Median :54.47 Median :54.82 Median :59.14
Mean :54.06 Mean :54.42 Mean :55.14 Mean :59.06
Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22.
Min. :60.91 Min. :61.30 Min. :59.00 Min. :51.25
1st Qu.:61.73 1st Qu.:62.57 1st Qu.:60.73 1st Qu.:51.98
Median :62.52 Median :63.87 Median :63.27 Median :53.73
Mean :62.49 Mean :64.01 Mean :63.77 Mean :56.44
Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11.
Min. :0 Min. :0.32 Min. :0.32 Min. :0.32
1st Qu.:0 1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.32
Median :0 Median :0.32 Median :0.32 Median :0.32
Mean :0 Mean :0.32 Mean :0.32 Mean :0.32
Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15.
Min. :0.30 Min. :0.29 Min. :0.27 Min. :0.27
1st Qu.:0.30 1st Qu.:0.29 1st Qu.:0.27 1st Qu.:0.27
Median :0.30 Median :0.29 Median :0.27 Median :0.27
Mean :0.30 Mean :0.29 Mean :0.27 Mean :0.27
Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19.
Min. :0.27 Min. :0.27 Min. :0.29 Min. :0.30
1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.29 1st Qu.:0.31
Median :0.27 Median :0.27 Median :0.30 Median :0.31
Mean :0.27 Mean :0.28 Mean :0.30 Mean :0.31
Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23. SWmm.24.
Min. :0.31 Min. :0.30 Min. :0.26 Min. :0 Min. :58.00
1st Qu.:0.31 1st Qu.:0.30 1st Qu.:0.26 1st Qu.:0 1st Qu.:58.78
Median :0.32 Median :0.32 Median :0.27 Median :0 Median :60.00
Mean :0.32 Mean :0.32 Mean :0.28 Mean :0 Mean :59.43
SWmm.25. SWmm.26. SWmm.27. Soil.NO3N.24. Soil.NO3N.25. Soil.NO3N.26. Soil.NO3N.27.
Min. :58.00 Min. :58.00 Min. :58.00 Min. :0.04 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0.21 1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0.02
Median :60.00 Median :60.00 Median :60.00 Median :0.28 Median :0.19 Median :0.19 Median :0.19
Mean :59.76 Mean :59.75 Mean :59.75 Mean :1.10 Mean :1.03 Mean :1.03 Mean :1.04
Lucerne.Root.LengthDensity.24. Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
Lucerne.Root.LengthDensity.27. Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26.
Min. :0 Min. :14.00 Min. :14.00 Min. :14.00
1st Qu.:0 1st Qu.:14.78 1st Qu.:16.00 1st Qu.:16.00
Median :0 Median :16.00 Median :16.00 Median :16.00
Mean :0 Mean :15.43 Mean :15.76 Mean :15.75
Soil.SoilWater.ESW.27. Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
Min. :14.00 Min. : 0.00 Min. : 0.00
1st Qu.:16.00 1st Qu.: 13.00 1st Qu.: 0.00
Median :16.00 Median : 30.00 Median : 0.00
Mean :15.75 Mean : 33.22 Mean : 23.65
NodeNumber Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. Lucerne.Leaf.LAIFunction.Value..
Min. : 0.000 Min. : 0.000 Min. :0.05000
1st Qu.: 2.688 1st Qu.: 2.020 1st Qu.:0.05159
Median : 6.317 Median : 5.416 Median :1.00000
Mean : 8.911 Mean : 8.122 Mean :2.30092
[ reached getOption("max.print") -- omitted 3 rows ]head(df) # simulation resultsget sim names (different table)
merge names
remove unecessary variables
simNameDf <- as.data.frame (GetApsimNGTable(db.address,"_Simulations"))
myDb <- merge(df, simNameDf, by.x= c("SimulationID"), by.y= c("ID"))
#str(myDb)
head(myDb)summary(myDb) SimulationID Water Zone Clock.Today
Min. : 1.00 Length:56703 Length:56703 Min. :1979-01-01 12:00:00
1st Qu.:14.00 Class :character Class :character 1st Qu.:1998-11-20 12:00:00
Median :40.00 Mode :character Mode :character Median :2001-11-30 12:00:00
Mean :34.87 Mean :2004-04-22 12:38:26
DiagnosticsVariables.Script.AccumPlantN DiagnosticsVariables.Script.AccumMineralisation
Min. : 0.0 Min. : -0.1345
1st Qu.:113.7 1st Qu.: 48.0568
Median :278.8 Median : 97.2750
Mean :253.9 Mean :132.5251
DiagnosticsVariables.Script.AccumDenit DiagnosticsVariables.Script.AccumFert DiagnosticsVariables.Script.AccumLeach
Min. :-120.284 Min. :0 Min. :-127.0290
1st Qu.: -11.333 1st Qu.:0 1st Qu.: -2.3541
Median : -3.323 Median :0 Median : -0.1743
Mean : -11.726 Mean :0 Mean : -4.5402
DiagnosticsVariables.Script.AccumDetach DiagnosticsVariables.Script.DeltaSoilOMN
Min. :0 Min. :-12180.53
1st Qu.:0 1st Qu.: -200.92
Median :0 Median : -91.00
Mean :0 Mean : -327.08
DiagnosticsVariables.Script.DeltaSurfaceOMN DiagnosticsVariables.Script.DeltaSoilMineralN Lucerne.Root.NSupply.Fixation
Min. :-16.0000 Min. :-258.73 Min. :0
1st Qu.: 0.0000 1st Qu.:-250.13 1st Qu.:0
Median : 0.0000 Median :-170.57 Median :0
Mean : -0.5151 Mean :-146.22 Mean :0
Lucerne.Root.NSupply.Reallocation Lucerne.Root.NSupply.Retranslocation Lucerne.Root.NSupply.Uptake Soil.SoilWater.Eo
Min. :0 Min. :0 Min. :0 Min. : 0.05379
1st Qu.:0 1st Qu.:0 1st Qu.:0 1st Qu.: 1.54350
Median :0 Median :0 Median :0 Median : 2.86545
Mean :0 Mean :0 Mean :0 Mean : 3.24309
Soil.SoilWater.Es SWC DiagnosticsVariables.Script.DUL Soil.SoilWater.Drainage Soil.SoilWater.Runoff
Min. :0.0000 Min. : 82.53 Min. : 103.0 Min. : 0.0000 Min. : 0.00000
1st Qu.:0.3311 1st Qu.: 480.80 1st Qu.: 723.0 1st Qu.: 0.0000 1st Qu.: 0.00000
Median :0.5887 Median : 657.43 Median : 744.0 Median : 0.0000 Median : 0.00000
Mean :0.8110 Mean : 661.72 Mean : 815.2 Mean : 0.1053 Mean : 0.01482
DiagnosticsVariables.Script.OutFlowLat DiagnosticsVariables.Script.AccumEO DiagnosticsVariables.Script.AccumEP
Min. :0 Min. :-9185.882 Min. :-2437.1
1st Qu.:0 1st Qu.:-2923.566 1st Qu.: -971.0
Median :0 Median :-1652.845 Median : -504.9
Mean :0 Mean :-2071.465 Mean : -604.6
DiagnosticsVariables.Script.AccumES DiagnosticsVariables.Script.AccumDrainage DiagnosticsVariables.Script.AccumRunoff
Min. :-2096.227 Min. :-592.411 Min. :-217.719
1st Qu.: -755.592 1st Qu.: -57.094 1st Qu.: -8.244
Median : -425.968 Median : -7.275 Median : 0.000
Mean : -519.739 Mean : -65.213 Mean : -12.639
DiagnosticsVariables.Script.AccumRainfall DiagnosticsVariables.Script.AccumIrrigation
Min. : 0.0 Min. :0
1st Qu.: 278.5 1st Qu.:0
Median : 734.4 Median :0
Mean : 911.5 Mean :0
DiagnosticsVariables.Script.AccumOutflowLat DiagnosticsVariables.Script.SoilWaterDeficit Lucerne.Grain.Live.Wt
Min. :0 Min. :-624.63 Min. : 0.000
1st Qu.:0 1st Qu.:-228.45 1st Qu.: 0.000
Median :0 Median :-112.06 Median : 0.000
Mean :0 Mean :-153.36 Mean : 2.965
Lucerne.Shell.Live.Wt StemWt Lucerne.Stem.Live.Wt Lucerne.Grain.Live.N Lucerne.Shell.Live.N LeafWt
Min. : 0.000 Min. : 0 Min. : 0.0 Min. :0.00000 Min. :0.00000 Min. : 0.0
1st Qu.: 0.000 1st Qu.: 567 1st Qu.: 56.7 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 415.2
Median : 0.000 Median : 1467 Median : 146.7 Median :0.00000 Median :0.00000 Median : 854.4
Mean : 2.965 Mean : 2336 Mean : 233.6 Mean :0.08896 Mean :0.08896 Mean :1219.2
RootWt Lucerne.Leaf.Live.N Lucerne.Root.Live.N Lucerne.Leaf.Live.NConc Lucerne.Root.Live.NConc
Min. : 0.0 Min. :0.000e+00 Min. :0.0000 Min. :0.00e+00 Min. :0.000000
1st Qu.: 149.4 1st Qu.:0.000e+00 1st Qu.:0.1472 1st Qu.:0.00e+00 1st Qu.:0.009827
Median : 275.3 Median :1.020e-08 Median :0.2709 Median :0.00e+00 Median :0.009935
Mean : 412.0 Mean :1.012e-04 Mean :0.4081 Mean :1.59e-05 Mean :0.009691
Lucerne.Root.WaterUptake ET Lucerne.Root.Depth Lucerne.Leaf.CoverTotal Lucerne.Leaf.CoverDead
Min. :0.00000 Min. : 0.0000 Min. : 0 Min. :0.00000 Min. :0
1st Qu.:0.05364 1st Qu.: 0.6631 1st Qu.:1500 1st Qu.:0.04093 1st Qu.:0
Median :0.42055 Median : 1.3066 Median :2300 Median :0.55514 Median :0
Mean :1.00188 Mean : 1.8129 Mean :1992 Mean :0.55734 Mean :0
LAI Height SWmm.1. SWmm.2. SWmm.3. SWmm.4. SWmm.5.
Min. :0.00000 Min. : 0.00 Min. : 1.00 Min. : 2.00 Min. : 5.00 Min. : 7.826 Min. : 7.389
1st Qu.:0.05159 1st Qu.: 82.63 1st Qu.:20.07 1st Qu.: 22.11 1st Qu.: 23.12 1st Qu.: 23.590 1st Qu.: 25.299
Median :1.00000 Median : 222.92 Median :29.36 Median : 32.63 Median : 29.89 Median : 28.929 Median : 27.807
Mean :2.29835 Mean : 314.30 Mean :29.57 Mean : 32.36 Mean : 36.09 Mean : 41.060 Mean : 43.080
SWmm.6. SWmm.7. SWmm.8. SWmm.9. SWmm.10. SWmm.11.
Min. : 7.291 Min. : 7.227 Min. : 7.33 Min. : 7.222 Min. : 7.313 Min. : 7.59
1st Qu.: 25.611 1st Qu.: 24.288 1st Qu.: 22.89 1st Qu.: 22.218 1st Qu.: 20.757 1st Qu.: 19.73
Median : 29.683 Median : 31.479 Median : 30.82 Median : 31.464 Median : 31.341 Median : 31.47
Mean : 44.685 Mean : 46.412 Mean : 45.17 Mean : 44.535 Mean : 44.817 Mean : 39.12
SWmm.12. SWmm.13. SWmm.14. SWmm.15. SWmm.16. SWmm.17. SWmm.18.
Min. : 8.58 Min. : 8.68 Min. : 9.10 Min. :10.00 Min. :10.23 Min. : 9.83 Min. : 9.78
1st Qu.: 20.67 1st Qu.:20.06 1st Qu.:19.55 1st Qu.:20.23 1st Qu.:21.79 1st Qu.:25.25 1st Qu.:25.24
Median : 29.86 Median :28.00 Median :29.81 Median :30.92 Median :30.76 Median :29.39 Median :29.97
Mean : 31.15 Mean :29.64 Mean :29.97 Mean :30.64 Mean :31.60 Mean :29.77 Mean :30.01
SWmm.19. SWmm.20. SWmm.21. SWmm.22. SWmm.23. Soil.NO3N.1. Soil.NO3N.2.
Min. : 9.72 Min. : 9.99 Min. :10.18 Min. :10.69 Min. :15.03 Min. : 0.000 Min. : 0.1319
1st Qu.:25.26 1st Qu.:25.34 1st Qu.:23.55 1st Qu.:18.39 1st Qu.:21.84 1st Qu.: 1.431 1st Qu.: 1.5505
Median :29.94 Median :30.55 Median :30.63 Median :30.80 Median :33.15 Median : 2.600 Median : 2.4889
Mean :30.22 Mean :30.52 Mean :30.61 Mean :29.32 Mean :31.69 Mean : 13.187 Mean : 6.3640
Soil.NO3N.3. Soil.NO3N.4. Soil.NO3N.5. Soil.NO3N.6. Soil.NO3N.7. Soil.NO3N.8. Soil.NO3N.9.
Min. : 0.2321 Min. : 0.2857 Min. : 0.3562 Min. : 0.486 Min. : 0.426 Min. : 0.432 Min. : 0.281
1st Qu.: 1.5254 1st Qu.: 1.2642 1st Qu.: 1.2458 1st Qu.: 1.279 1st Qu.: 1.042 1st Qu.: 1.007 1st Qu.: 0.934
Median : 2.4029 Median : 1.9555 Median : 1.7426 Median : 1.760 Median : 1.499 Median : 1.364 Median : 1.223
Mean : 5.8377 Mean : 4.5441 Mean : 3.8181 Mean : 3.104 Mean : 2.523 Mean : 1.821 Mean : 1.675
Soil.NO3N.10. Soil.NO3N.11. Soil.NO3N.12. Soil.NO3N.13. Soil.NO3N.14. Soil.NO3N.15. Soil.NO3N.16.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.058
1st Qu.: 0.609 1st Qu.: 0.468 1st Qu.: 0.334 1st Qu.: 0.255 1st Qu.: 0.224 1st Qu.: 0.233 1st Qu.: 0.442
Median : 0.837 Median : 0.708 Median : 0.584 Median : 0.531 Median : 0.506 Median : 0.473 Median : 0.515
Mean : 1.316 Mean : 1.388 Mean : 1.161 Mean : 1.136 Mean : 1.101 Mean : 1.073 Mean : 1.220
Soil.NO3N.17. Soil.NO3N.18. Soil.NO3N.19. Soil.NO3N.20. Soil.NO3N.21. Soil.NO3N.22. Soil.NO3N.23.
Min. :0.149 Min. :0.098 Min. :0.074 Min. :0.095 Min. :0.098 Min. :0.097 Min. :0.140
1st Qu.:0.373 1st Qu.:0.345 1st Qu.:0.320 1st Qu.:0.310 1st Qu.:0.315 1st Qu.:0.335 1st Qu.:0.379
Median :0.468 Median :0.435 Median :0.417 Median :0.405 Median :0.412 Median :0.464 Median :0.478
Mean :0.779 Mean :0.738 Mean :0.739 Mean :0.751 Mean :0.796 Mean :0.821 Mean :0.806
DiagnosticsVariables.Script.SoilNitrogenContent Lucerne.Arbitrator.N.TotalPlantDemand DiagnosticsVariables.Script.FomN
Min. : 0.00 Min. :0.00000 Min. : 0.0000
1st Qu.: 20.47 1st Qu.:0.01241 1st Qu.: 0.3537
Median : 26.75 Median :0.08534 Median : 1.0975
Mean : 54.01 Mean :0.18648 Mean : 3.3132
DiagnosticsVariables.Script.HumN DiagnosticsVariables.Script.BiomN DiagnosticsVariables.Script.DltNMinRes
Min. : 0 Min. : 0.00 Min. :-1.679280
1st Qu.:11015 1st Qu.: 87.43 1st Qu.: 0.000000
Median :22728 Median :128.38 Median : 0.000000
Mean :17206 Mean :126.37 Mean :-0.001052
DiagnosticsVariables.Script.DltNMinTot Lucerne.Leaf.Fw Lucerne.Leaf.Fn Lucerne.Phenology.CurrentPhaseName
Min. :-0.07311 Min. :0.0000 Min. :0.0000000 Length:56703
1st Qu.: 0.07441 1st Qu.:1.0000 1st Qu.:0.0000000 Class :character
Median : 0.15414 Median :1.0000 Median :0.0000001 Mode :character
Mean : 0.19326 Mean :0.8591 Mean :0.0532373
Lucerne.Phenology.CurrentStageName Lucerne.Phenology.Stage Lucerne.Pod.Wt Lucerne.Pod.N shootbiomass
Length:56703 Min. :1.000 Min. : 0.000 Min. : 0.0000 Min. : 0
Class :character 1st Qu.:4.310 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 1008
Mode :character Median :4.808 Median : 0.000 Median : 0.0000 Median : 2316
Mean :4.939 Mean : 5.931 Mean : 0.1779 Mean : 3614
Lucerne.Root.LengthDensity.1. Lucerne.Root.LengthDensity.2. Lucerne.Root.LengthDensity.3. Lucerne.Root.LengthDensity.4.
Min. :0.000000 Min. :0.0000000 Min. :0.0000000 Min. :0.0000000
1st Qu.:0.000561 1st Qu.:0.0004222 1st Qu.:0.0002453 1st Qu.:0.0001412
Median :0.001456 Median :0.0010220 Median :0.0006460 Median :0.0004311
Mean :0.003661 Mean :0.0016247 Mean :0.0010904 Mean :0.0007684
Lucerne.Root.LengthDensity.5. Lucerne.Root.LengthDensity.6. Lucerne.Root.LengthDensity.7. Lucerne.Root.LengthDensity.8.
Min. :0.000e+00 Min. :0.0000 Min. :0.0000 Min. :0.000
1st Qu.:5.606e-05 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
Median :3.537e-04 Median :0.0003 Median :0.0003 Median :0.000
Mean :6.366e-04 Mean :0.0006 Mean :0.0006 Mean :0.001
Lucerne.Root.LengthDensity.9. Lucerne.Root.LengthDensity.10. Lucerne.Root.LengthDensity.11.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.001 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.12. Lucerne.Root.LengthDensity.13. Lucerne.Root.LengthDensity.14.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.15. Lucerne.Root.LengthDensity.16. Lucerne.Root.LengthDensity.17.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.18. Lucerne.Root.LengthDensity.19. Lucerne.Root.LengthDensity.20.
Min. :0.000 Min. :0.000 Min. :0.000
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000
Median :0.000 Median :0.000 Median :0.000
Mean :0.000 Mean :0.000 Mean :0.000
Lucerne.Root.LengthDensity.21. Lucerne.Root.LengthDensity.22. Lucerne.Root.LengthDensity.23. Soil.SoilWater.WaterTable
Min. :0.000 Min. :0.000 Min. :0.000 Min. : 0.763
1st Qu.:0.000 1st Qu.:0.000 1st Qu.:0.000 1st Qu.:1500.000
Median :0.000 Median :0.000 Median :0.000 Median :2300.000
Mean :0.000 Mean :0.000 Mean :0.000 Mean :2043.145
Lucerne.AboveGround.Wt Lucerne.AboveGround.N Soil.SoilWater.ESW.1. Soil.SoilWater.ESW.2. Soil.SoilWater.ESW.3.
Min. : 0.0 Min. : 0.000 Min. : 0.00 Min. : 0.000 Min. : 0.00
1st Qu.: 100.8 1st Qu.: 1.709 1st Qu.: 6.85 1st Qu.: 8.134 1st Qu.: 6.00
Median : 231.6 Median : 4.422 Median :16.71 Median :19.774 Median :17.85
Mean : 361.4 Mean : 7.140 Mean :15.63 Mean :17.125 Mean :16.00
Soil.SoilWater.ESW.4. Soil.SoilWater.ESW.5. Soil.SoilWater.ESW.6. Soil.SoilWater.ESW.7. Soil.SoilWater.ESW.8.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.00
1st Qu.: 3.823 1st Qu.: 2.194 1st Qu.: 1.203 1st Qu.: 2.039 1st Qu.: 3.33
Median :16.289 Median : 14.646 Median :15.028 Median :14.378 Median :14.72
Mean :14.744 Mean : 13.601 Mean :14.014 Mean :13.349 Mean :14.69
Soil.SoilWater.ESW.9. Soil.SoilWater.ESW.10. Soil.SoilWater.ESW.11. Soil.SoilWater.ESW.12. Soil.SoilWater.ESW.13.
Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
1st Qu.: 2.195 1st Qu.: 1.617 1st Qu.: 2.878 1st Qu.: 8.027 1st Qu.: 7.445
Median :13.891 Median :13.651 Median :15.098 Median :16.565 Median :15.733
Mean :13.528 Mean :13.655 Mean :14.100 Mean :15.615 Mean :14.553
Soil.SoilWater.ESW.14. Soil.SoilWater.ESW.15. Soil.SoilWater.ESW.16. Soil.SoilWater.ESW.17. Soil.SoilWater.ESW.18.
Min. : 0.000 Min. : 0.000 Min. : 0.00 Min. : 0.00 Min. : 0.00
1st Qu.: 7.172 1st Qu.: 8.141 1st Qu.:11.02 1st Qu.:14.00 1st Qu.:14.00
Median :16.238 Median :16.571 Median :16.94 Median :18.69 Median :18.63
Mean :14.820 Mean :15.538 Mean :15.72 Mean :16.90 Mean :17.14
Soil.SoilWater.ESW.19. Soil.SoilWater.ESW.20. Soil.SoilWater.ESW.21. Soil.SoilWater.ESW.22. Soil.SoilWater.ESW.23.
Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 6.025
1st Qu.:14.00 1st Qu.:15.72 1st Qu.:14.02 1st Qu.: 8.32 1st Qu.:12.608
Median :18.90 Median :20.30 Median :18.98 Median :18.16 Median :21.281
Mean :17.36 Mean :18.30 Mean :17.57 Mean :16.26 Mean :19.715
CheckpointID SowingDate Defoliation FD Factors Soil.OutputLayers.SWmm.1.
Min. :1 Length:56703 Length:56703 Length:56703 Length:56703 Min. : 3.00
1st Qu.:1 Class :character Class :character Class :character Class :character 1st Qu.: 14.34
Median :1 Mode :character Mode :character Mode :character Mode :character Median : 21.67
Mean :1 Mean : 25.46
Soil.OutputLayers.SWmm.2. Soil.OutputLayers.SWmm.3. Soil.OutputLayers.SWmm.4. Soil.OutputLayers.SWmm.5.
Min. : 21.00 Min. : 42.09 Min. : 0.00 Min. : 51.35
1st Qu.: 29.67 1st Qu.: 55.27 1st Qu.: 52.00 1st Qu.: 86.27
Median : 63.91 Median : 71.42 Median : 80.01 Median :119.08
Mean : 76.81 Mean : 87.61 Mean : 87.10 Mean :117.11
Soil.OutputLayers.SWmm.6. Soil.OutputLayers.SWmm.7. Soil.OutputLayers.SWmm.8. Soil.OutputLayers.SW.1.
Min. : 54.79 Min. : 57.97 Min. : 60.89 Min. :0.03
1st Qu.: 74.11 1st Qu.: 90.00 1st Qu.: 88.00 1st Qu.:0.07
Median : 93.97 Median : 99.01 Median : 92.08 Median :0.18
Mean :105.21 Mean :108.70 Mean :137.56 Mean :0.19
Soil.OutputLayers.SW.2. Soil.OutputLayers.SW.3. Soil.OutputLayers.SW.4. Soil.OutputLayers.SW.5. Soil.OutputLayers.SW.6.
Min. :0.05 Min. :0.05 Min. :0.00 Min. :0.22 Min. :0.19
1st Qu.:0.10 1st Qu.:0.16 1st Qu.:0.20 1st Qu.:0.24 1st Qu.:0.21
Median :0.23 Median :0.24 Median :0.25 Median :0.29 Median :0.28
Mean :0.23 Mean :0.24 Mean :0.25 Mean :0.30 Mean :0.29
Soil.OutputLayers.SW.7. Soil.OutputLayers.SW.8. Soil.OutputLayers.SWmm.9. Soil.OutputLayers.SWmm.10.
Min. :0.22 Min. :0.22 Min. :63.01 Min. :64.02
1st Qu.:0.23 1st Qu.:0.22 1st Qu.:63.35 1st Qu.:64.34
Median :0.29 Median :0.31 Median :63.71 Median :64.77
Mean :0.30 Mean :0.30 Mean :63.67 Mean :64.87
Soil.OutputLayers.SWmm.11. Soil.OutputLayers.SWmm.12. Soil.OutputLayers.SWmm.13. Soil.OutputLayers.SWmm.14.
Min. :64.00 Min. :60.11 Min. :58.00 Min. :54.00
1st Qu.:64.00 1st Qu.:60.19 1st Qu.:58.00 1st Qu.:54.02
Median :64.00 Median :60.36 Median :58.00 Median :54.20
Mean :64.36 Mean :60.74 Mean :58.00 Mean :54.24
Soil.OutputLayers.SWmm.15. Soil.OutputLayers.SWmm.16. Soil.OutputLayers.SWmm.17. Soil.OutputLayers.SWmm.18.
Min. :54.00 Min. :54.00 Min. :54.53 Min. :58.01
1st Qu.:54.02 1st Qu.:54.38 1st Qu.:54.68 1st Qu.:58.85
Median :54.08 Median :54.47 Median :54.82 Median :59.14
Mean :54.06 Mean :54.42 Mean :55.14 Mean :59.06
Soil.OutputLayers.SWmm.19. Soil.OutputLayers.SWmm.20. Soil.OutputLayers.SWmm.21. Soil.OutputLayers.SWmm.22.
Min. :60.91 Min. :61.30 Min. :59.00 Min. :51.25
1st Qu.:61.73 1st Qu.:62.57 1st Qu.:60.73 1st Qu.:51.98
Median :62.52 Median :63.87 Median :63.27 Median :53.73
Mean :62.49 Mean :64.01 Mean :63.77 Mean :56.44
Soil.OutputLayers.SWmm.23. Soil.OutputLayers.SW.9. Soil.OutputLayers.SW.10. Soil.OutputLayers.SW.11.
Min. :0 Min. :0.32 Min. :0.32 Min. :0.32
1st Qu.:0 1st Qu.:0.32 1st Qu.:0.32 1st Qu.:0.32
Median :0 Median :0.32 Median :0.32 Median :0.32
Mean :0 Mean :0.32 Mean :0.32 Mean :0.32
Soil.OutputLayers.SW.12. Soil.OutputLayers.SW.13. Soil.OutputLayers.SW.14. Soil.OutputLayers.SW.15.
Min. :0.30 Min. :0.29 Min. :0.27 Min. :0.27
1st Qu.:0.30 1st Qu.:0.29 1st Qu.:0.27 1st Qu.:0.27
Median :0.30 Median :0.29 Median :0.27 Median :0.27
Mean :0.30 Mean :0.29 Mean :0.27 Mean :0.27
Soil.OutputLayers.SW.16. Soil.OutputLayers.SW.17. Soil.OutputLayers.SW.18. Soil.OutputLayers.SW.19.
Min. :0.27 Min. :0.27 Min. :0.29 Min. :0.30
1st Qu.:0.27 1st Qu.:0.27 1st Qu.:0.29 1st Qu.:0.31
Median :0.27 Median :0.27 Median :0.30 Median :0.31
Mean :0.27 Mean :0.28 Mean :0.30 Mean :0.31
Soil.OutputLayers.SW.20. Soil.OutputLayers.SW.21. Soil.OutputLayers.SW.22. Soil.OutputLayers.SW.23. SWmm.24.
Min. :0.31 Min. :0.30 Min. :0.26 Min. :0 Min. :58.00
1st Qu.:0.31 1st Qu.:0.30 1st Qu.:0.26 1st Qu.:0 1st Qu.:58.78
Median :0.32 Median :0.32 Median :0.27 Median :0 Median :60.00
Mean :0.32 Mean :0.32 Mean :0.28 Mean :0 Mean :59.43
SWmm.25. SWmm.26. SWmm.27. Soil.NO3N.24. Soil.NO3N.25. Soil.NO3N.26. Soil.NO3N.27.
Min. :58.00 Min. :58.00 Min. :58.00 Min. :0.04 Min. :0.00 Min. :0.00 Min. :0.00
1st Qu.:60.00 1st Qu.:60.00 1st Qu.:60.00 1st Qu.:0.21 1st Qu.:0.02 1st Qu.:0.02 1st Qu.:0.02
Median :60.00 Median :60.00 Median :60.00 Median :0.28 Median :0.19 Median :0.19 Median :0.19
Mean :59.76 Mean :59.75 Mean :59.75 Mean :1.10 Mean :1.03 Mean :1.03 Mean :1.04
Lucerne.Root.LengthDensity.24. Lucerne.Root.LengthDensity.25. Lucerne.Root.LengthDensity.26.
Min. :0 Min. :0 Min. :0
1st Qu.:0 1st Qu.:0 1st Qu.:0
Median :0 Median :0 Median :0
Mean :0 Mean :0 Mean :0
Lucerne.Root.LengthDensity.27. Soil.SoilWater.ESW.24. Soil.SoilWater.ESW.25. Soil.SoilWater.ESW.26.
Min. :0 Min. :14.00 Min. :14.00 Min. :14.00
1st Qu.:0 1st Qu.:14.78 1st Qu.:16.00 1st Qu.:16.00
Median :0 Median :16.00 Median :16.00 Median :16.00
Mean :0 Mean :15.43 Mean :15.76 Mean :15.75
Soil.SoilWater.ESW.27. Lucerne.Phenology.DaysAfterCutting.Value.. Lucerne.Phenology.FloweringDaysAfterCutting.Value..
Min. :14.00 Min. : 0.00 Min. : 0.00
1st Qu.:16.00 1st Qu.: 13.00 1st Qu.: 0.00
Median :16.00 Median : 30.00 Median : 0.00
Mean :15.75 Mean : 33.22 Mean : 23.65
NodeNumber Lucerne.Leaf.HeightFunction.DeltaHeight.Value.. Lucerne.Leaf.LAIFunction.Value.. Name
Min. : 0.000 Min. : 0.000 Min. :0.05000 Length:56703
1st Qu.: 2.688 1st Qu.: 2.020 1st Qu.:0.05159 Class :character
Median : 6.317 Median : 5.416 Median :1.00000 Mode :character
Mean : 8.911 Mean : 8.122 Mean :2.30092
[ reached getOption("max.print") -- omitted 3 rows ]# myDb %>%
# dplyr::select(Name) %>%
# unique()Prepare merge
Add info for merging
select variables that are for comparing with observed data
simD <- myDb %>%
dplyr::select(Name,Clock.Today,LAI,SWC,Height,shootbiomass,RootWt, StemWt, LeafWt,NodeNumber) %>%
tidyr::gather("Variable","Predicted",LAI:NodeNumber) %>%
mutate(Name = as.factor(Name)) %>%
mutate(Variable = as.factor(Variable)) %>%
mutate(Clock.Today = ymd_hms(Clock.Today))
head(simD)summary(simD) Name Clock.Today Variable Predicted
RutherglenDefoliation: 20448 Min. :1979-01-01 12:00:00 Height : 56703 Min. : 0.00
WarraSowingDateSD1 : 16016 1st Qu.:1998-11-20 12:00:00 LAI : 56703 1st Qu.: 11.86
HudsonDefoliation : 15984 Median :2001-11-30 12:00:00 LeafWt : 56703 Median : 383.81
Iversen_8Waterdry : 15152 Mean :2004-04-22 12:38:26 NodeNumber : 56703 Mean : 1071.01
Iversen_8Waterirr : 15152 3rd Qu.:2012-03-15 12:00:00 RootWt : 56703 3rd Qu.: 1040.32
TamworthDefoliation : 13208 Max. :2018-01-16 12:00:00 shootbiomass: 56703 Max. :29003.89
(Other) :357664 (Other) :113406 head(ObsH)mergedf<-merge(obsHN,simD,by=c("Clock.Today","Name","Variable"))
summary(mergedf) Clock.Today Name Variable ExpUnitCode
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5 :108 Height :408 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14
1st Qu.:2001-02-20 Iversen_91DefoliationLL :107 Branch : 0 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12
Median :2002-10-25 Iversen_8Waterirr : 68 Fraction : 0 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11
Mean :2005-09-17 Iversen_9SowingDateSD1Waterirr: 67 HardStemWt: 0 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11
3rd Qu.:2015-02-09 Iversen_9SowingDateSD2Waterirr: 25 LAI : 0 Iversen_8WaterirrGs_5Rt_1 : 10
Max. :2018-01-15 Iversen_9SowingDateSD3Waterirr: 18 LeafWt : 0 Iversen_91DefoliationLLGs_2Rt_1 : 10
(Other) : 15 (Other) : 0 (Other) :340
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x GrowthSeason.x Rotation.x
1997_2001: 68 Lincoln2000:131 dry: 0 HH: 0 No :175 FD10: 0 Min. :1.000 Min. :1.000
2000_2002:125 Lincoln2015:108 irr:408 LL:408 no :108 FD2 : 0 1st Qu.:1.000 1st Qu.:1.000
2002_2004:107 Lincoln2003: 54 SD1 : 67 FD5 :408 Median :2.000 Median :3.000
2010_2012: 0 Lincoln2001: 35 SD2 : 25 Mean :2.105 Mean :3.098
2014_2018:108 Lincoln2004: 31 SD3 : 18 3rd Qu.:3.000 3rd Qu.:5.000
2014_2019: 0 Lincoln2002: 22 SD4 : 15 Max. :5.000 Max. :7.000
(Other) : 27 (Other): 0
StartDate MidDate FinishDate Interval VariableUnits Time
Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19 Min. : 0.00 % : 0 12:00:00:408
1st Qu.:2001-01-24 1st Qu.:2001-02-13 1st Qu.:2001-03-23 1st Qu.: 14.75 cm :233
Median :2002-10-06 Median :2002-10-26 Median :2002-11-16 Median : 28.00 fractio0l: 0
Mean :2005-08-16 Mean :2005-09-14 Mean :2005-10-14 Mean : 32.18 Fraction : 0
3rd Qu.:2015-01-30 3rd Qu.:2015-02-19 3rd Qu.:2015-03-11 3rd Qu.: 41.00 kg/ha : 0
Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :116.00 m2/m2 : 0
mm :175
Observed StdDEV GrowthSeason1 Rotation1 Clock.Today1 year day
Min. : 0.00 Min. : 0.000 Gs_1:160 Rt_1:112 Min. :1997-10-23 12:00:00 Min. :1997 Min. : 1.0
1st Qu.: 62.12 1st Qu.: 0.000 Gs_2:140 Rt_2: 80 1st Qu.:2001-02-20 12:00:00 1st Qu.:2001 1st Qu.: 63.0
Median :185.00 Median : 3.107 Gs_3: 54 Rt_3: 59 Median :2002-10-25 12:00:00 Median :2002 Median :138.5
Mean :219.48 Mean :12.001 Gs_4: 13 Rt_4: 50 Mean :2005-09-17 22:25:44 Mean :2005 Mean :170.4
3rd Qu.:360.50 3rd Qu.:16.955 Gs_5: 41 Rt_5: 46 3rd Qu.:2015-02-10 06:00:00 3rd Qu.:2015 3rd Qu.:292.0
Max. :798.00 Max. :91.520 Gs_6: 0 Rt_6: 42 Max. :2018-01-15 12:00:00 Max. :2018 Max. :365.0
NA's :229 Rt_7: 19
rain maxt mint mean radn wind vp
Min. : 0.000 Min. : 7.90 Min. :-4.900 Min. : 2.55 Min. : 1.50 Min. :0.700 Min. : 5.10
1st Qu.: 0.000 1st Qu.:15.32 1st Qu.: 4.375 1st Qu.:10.40 1st Qu.: 9.70 1st Qu.:2.900 1st Qu.:10.00
Median : 0.000 Median :19.00 Median : 8.700 Median :13.20 Median :16.14 Median :3.900 Median :11.40
Mean : 0.876 Mean :19.17 Mean : 7.853 Mean :13.49 Mean :16.73 Mean :3.997 Mean :11.77
3rd Qu.: 0.000 3rd Qu.:22.23 3rd Qu.:11.300 3rd Qu.:16.50 3rd Qu.:22.62 3rd Qu.:4.900 3rd Qu.:13.72
Max. :31.800 Max. :33.80 Max. :20.600 Max. :26.20 Max. :33.40 Max. :9.300 Max. :22.00
Pp Tb TTbeta Tbb TTbroken TbF TTfick
Min. :10.02 Min. :1 Min. : 0.128 Min. :1 Min. : 1.841 Min. :1 Min. : 2.222
1st Qu.:12.21 1st Qu.:1 1st Qu.: 2.247 1st Qu.:1 1st Qu.: 6.822 1st Qu.:1 1st Qu.: 8.172
Median :14.27 Median :1 Median : 4.118 Median :1 Median : 9.015 Median :1 Median :10.646
Mean :13.93 Mean :1 Mean : 5.812 Mean :1 Mean : 9.535 Mean :1 Mean :10.878
3rd Qu.:15.82 3rd Qu.:1 3rd Qu.: 8.085 3rd Qu.:1 3rd Qu.:12.040 3rd Qu.:1 3rd Qu.:13.588
Max. :16.65 Max. :1 Max. :23.343 Max. :1 Max. :20.258 Max. :1 Max. :20.813
ExpName Experiment.y Water.y Defoliation.y SowingDate.y FD.y
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Lincoln1997: 68 dry: 0 HH: 0 No :175 FD10: 0
Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Lincoln2000:110 irr:408 LL:408 no :108 FD2 : 0
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Lincoln2001: 15 LS: 0 SD1 : 67 FD5 :408
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Lincoln2002:107 SL: 0 SD2 : 25
Iversen_8WaterirrGs_5Rt_1 : 10 Lincoln2010: 0 SS: 0 SD3 : 18
Iversen_91DefoliationLLGs_2Rt_1 : 10 Lincoln2015:108 SD4 : 15
(Other) :340 (Other): 0
GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean
Gs_1:160 Rt_1 :112 Min. : 0.2834 Min. : 4.022 Min. : 3.333 Min. :10.25 Min. : 7.024
Gs_2:140 Rt_2 : 80 1st Qu.: 60.8023 1st Qu.: 165.210 1st Qu.: 142.690 1st Qu.:11.99 1st Qu.:10.974
Gs_3: 54 Rt_3 : 59 Median :123.4401 Median : 299.224 Median : 258.979 Median :14.48 Median :13.963
Gs_4: 13 Rt_4 : 50 Mean :157.2934 Mean : 326.606 Mean : 283.618 Mean :13.84 Mean :13.226
Gs_5: 41 Rt_5 : 46 3rd Qu.:218.2569 3rd Qu.: 453.159 3rd Qu.: 395.238 3rd Qu.:15.99 3rd Qu.:15.771
Gs_6: 0 Rt_6 : 42 Max. :722.6708 Max. :1186.654 Max. :1051.876 Max. :16.55 Max. :19.327
(Other): 19
GrowthRotation Predicted
11 : 60 Min. : 0.0
12 : 38 1st Qu.:103.4
21 : 24 Median :222.0
26 : 24 Mean :266.7
13 : 23 3rd Qu.:403.0
22 : 23 Max. :858.6
(Other):216 str(mergedf)'data.frame': 408 obs. of 54 variables:
$ Clock.Today : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
$ Variable : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
$ ExpUnitCode : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
$ Collection : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment.x : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Water.x : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.x : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.x : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.x: int 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.x : int 2 2 2 2 2 3 3 3 4 4 ...
$ StartDate : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
$ MidDate : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
$ FinishDate : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
$ Interval : int 16 21 27 34 41 19 28 33 22 30 ...
$ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 62.7 146.7 320.7 436.7 564.3 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
$ Clock.Today1 : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
$ year : int 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
$ day : int 296 301 307 314 321 343 352 357 15 23 ...
$ rain : num 0 0.7 0 0 0 0 0 0 0 0 ...
$ maxt : num 15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
$ mint : num 8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
$ mean : num 12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
$ radn : num 20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
$ wind : num 4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
$ vp : num 10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
$ Pp : num 14.6 14.9 15.2 15.5 15.8 ...
$ Tb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbeta : num 2.652 8.148 9.211 0.944 4.723 ...
$ Tbb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbroken : num 7.87 11.44 11.84 5.3 9.66 ...
$ TbF : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTfick : num 9.49 12.74 13.02 6.4 11.33 ...
$ ExpName : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
$ Experiment.y : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water.y : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.y : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.y : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.y : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
$ Tt_beta_sum : num 69.7 96.2 141.7 172.5 217.7 ...
$ Tt_fick_sum : num 166 220 295 363 447 ...
$ Tt_broken_sum : num 142 189 256 315 388 ...
$ Ppm : num 14.9 14.9 14.9 14.9 14.9 ...
$ Tmean : num 13.2 13.2 13.2 13.2 13.2 ...
$ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
$ Predicted : num 187 254 355 443 554 ...mergedfNode number
Time series
obs Vs Pre for each experiment
1997-2001
obsheight1<-ObsH%>%dplyr::filter(Name=="Iversen_8Waterirr")
simD1<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="Height")%>%
dplyr::filter(Name=="Iversen_8Waterirr")
str(simD1)'data.frame': 1894 obs. of 4 variables:
$ Name : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 35 35 35 35 35 35 35 35 35 35 ...
$ Clock.Today: POSIXct, format: "2000-03-08 12:00:00" "1997-11-30 12:00:00" "1997-02-09 12:00:00" "1998-04-06 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Predicted : num 668.9 203.4 894.4 243.1 25.7 ... simD1%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~Name,ncol = 2)+
geom_point(data=obsheight1, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
2002-2004
obsheight2<-obsHN%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")%>%
dplyr::filter(Variable=="Height")
simD2<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="Height")%>%
dplyr::filter(Name=="Iversen_91DefoliationLL")%>%
dplyr::filter(Clock.Today>"2002-06-01")
str(simD2)'data.frame': 884 obs. of 4 variables:
$ Name : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 36 36 36 36 36 36 36 36 36 36 ...
$ Clock.Today: POSIXct, format: "2004-08-06 12:00:00" "2003-05-04 12:00:00" "2004-08-07 12:00:00" "2004-05-04 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Predicted : num 92.86 231.16 95.46 8.71 328.66 ...simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~Name,ncol = 1)+
geom_point(data=obsheight2, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NA
NAobsheight2<-obsHN%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")%>%
dplyr::filter(Variable=="Height")
simD2<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Variable=="Height")%>%
dplyr::filter(Name=="Iversen_121DefoliationLLFDFD5")
str(simD2)'data.frame': 1197 obs. of 4 variables:
$ Name : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 20 20 20 20 20 20 20 20 20 20 ...
$ Clock.Today: POSIXct, format: "2014-10-27 12:00:00" "2014-12-09 12:00:00" "2014-10-11 12:00:00" "2014-10-16 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Predicted : num 0 215.7 0 0 14.5 ...simD2%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~Name,ncol = 1)+
geom_point(data=obsheight2, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NA
NA2000-2002
obsheight3<-obsHN%>%
dplyr::filter(Collection=="2000_2002")%>%
dplyr::filter(Variable=="Height")
simD3<-simD%>%
mutate(Clock.Today = ymd_hms(Clock.Today))%>%
dplyr::filter(Clock.Today>"2000-10-24 12:00:00")%>%
dplyr::filter(Clock.Today<"2002-07-01 12:00:00")%>%
dplyr::filter(Name!="Iversen_8Waterdry")%>%
dplyr::filter(Name!="Iversen_8Waterirr")%>%
dplyr::filter(Name!="Iversen_91DefoliationLL")%>%
dplyr::filter(Name!="Iversen_91DefoliationLS")%>%
dplyr::filter(Name!="Iversen_91DefoliationSL")%>%
dplyr::filter(Name!="Iversen_91DefoliationSS")%>%
dplyr::filter(Name!="Iversen_91DefoliationSS")%>%
dplyr::filter(Name!="MooraDefoliation")%>%
dplyr::filter(Name!="NekiaDefoliation")%>%
dplyr::filter(Name!="QuairadingDefoliation")%>%
dplyr::filter(Name!="RoseworthyWaterdry")%>%
dplyr::filter(Name!="RoseworthyWaterirr")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD1Waterdry")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD2Waterdry")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD3Waterdry")%>%
dplyr::filter(Name!="Iversen_9SowingDateSD4Waterdry")%>%
dplyr::filter(Variable=="Height")
str(simD3)'data.frame': 2333 obs. of 4 variables:
$ Name : Factor w/ 63 levels "AshleyDeneSowingDateSD1",..: 41 41 41 41 41 41 41 41 41 41 ...
$ Clock.Today: POSIXct, format: "2001-08-05 12:00:00" "2001-09-10 12:00:00" "2001-05-23 12:00:00" "2000-11-21 12:00:00" ...
$ Variable : Factor w/ 8 levels "Height","LAI",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Predicted : num 50.6 169 72.1 0 67 ...simD3%>%
ggplot(aes(x=Clock.Today,y=Predicted))+geom_line(size=1)+theme_bw()+
facet_wrap(~Name,ncol = 2)+
geom_point(data=obsheight3, aes(x=Clock.Today1, y=Observed),colour="green",size=3)+
facet_wrap(~Name,ncol = 2)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Date")+ylab("Plant height (mm)")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
NAStatistic and Graph
mergedfsummary(mergedf) Clock.Today Name Variable ExpUnitCode
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5 :108 Height :408 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14
1st Qu.:2001-02-20 Iversen_91DefoliationLL :107 Branch : 0 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12
Median :2002-10-25 Iversen_8Waterirr : 68 Fraction : 0 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11
Mean :2005-09-17 Iversen_9SowingDateSD1Waterirr: 67 HardStemWt: 0 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11
3rd Qu.:2015-02-09 Iversen_9SowingDateSD2Waterirr: 25 LAI : 0 Iversen_8WaterirrGs_5Rt_1 : 10
Max. :2018-01-15 Iversen_9SowingDateSD3Waterirr: 18 LeafWt : 0 Iversen_91DefoliationLLGs_2Rt_1 : 10
(Other) : 15 (Other) : 0 (Other) :340
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x GrowthSeason.x Rotation.x
1997_2001: 68 Lincoln2000:131 dry: 0 HH: 0 No :175 FD10: 0 Min. :1.000 Min. :1.000
2000_2002:125 Lincoln2015:108 irr:408 LL:408 no :108 FD2 : 0 1st Qu.:1.000 1st Qu.:1.000
2002_2004:107 Lincoln2003: 54 SD1 : 67 FD5 :408 Median :2.000 Median :3.000
2010_2012: 0 Lincoln2001: 35 SD2 : 25 Mean :2.105 Mean :3.098
2014_2018:108 Lincoln2004: 31 SD3 : 18 3rd Qu.:3.000 3rd Qu.:5.000
2014_2019: 0 Lincoln2002: 22 SD4 : 15 Max. :5.000 Max. :7.000
(Other) : 27 (Other): 0
StartDate MidDate FinishDate Interval VariableUnits Time
Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19 Min. : 0.00 % : 0 12:00:00:408
1st Qu.:2001-01-24 1st Qu.:2001-02-13 1st Qu.:2001-03-23 1st Qu.: 14.75 cm :233
Median :2002-10-06 Median :2002-10-26 Median :2002-11-16 Median : 28.00 fractio0l: 0
Mean :2005-08-16 Mean :2005-09-14 Mean :2005-10-14 Mean : 32.18 Fraction : 0
3rd Qu.:2015-01-30 3rd Qu.:2015-02-19 3rd Qu.:2015-03-11 3rd Qu.: 41.00 kg/ha : 0
Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :116.00 m2/m2 : 0
mm :175
Observed StdDEV GrowthSeason1 Rotation1 Clock.Today1 year day
Min. : 0.00 Min. : 0.000 Gs_1:160 Rt_1:112 Min. :1997-10-23 12:00:00 Min. :1997 Min. : 1.0
1st Qu.: 62.12 1st Qu.: 0.000 Gs_2:140 Rt_2: 80 1st Qu.:2001-02-20 12:00:00 1st Qu.:2001 1st Qu.: 63.0
Median :185.00 Median : 3.107 Gs_3: 54 Rt_3: 59 Median :2002-10-25 12:00:00 Median :2002 Median :138.5
Mean :219.48 Mean :12.001 Gs_4: 13 Rt_4: 50 Mean :2005-09-17 22:25:44 Mean :2005 Mean :170.4
3rd Qu.:360.50 3rd Qu.:16.955 Gs_5: 41 Rt_5: 46 3rd Qu.:2015-02-10 06:00:00 3rd Qu.:2015 3rd Qu.:292.0
Max. :798.00 Max. :91.520 Gs_6: 0 Rt_6: 42 Max. :2018-01-15 12:00:00 Max. :2018 Max. :365.0
NA's :229 Rt_7: 19
rain maxt mint mean radn wind vp
Min. : 0.000 Min. : 7.90 Min. :-4.900 Min. : 2.55 Min. : 1.50 Min. :0.700 Min. : 5.10
1st Qu.: 0.000 1st Qu.:15.32 1st Qu.: 4.375 1st Qu.:10.40 1st Qu.: 9.70 1st Qu.:2.900 1st Qu.:10.00
Median : 0.000 Median :19.00 Median : 8.700 Median :13.20 Median :16.14 Median :3.900 Median :11.40
Mean : 0.876 Mean :19.17 Mean : 7.853 Mean :13.49 Mean :16.73 Mean :3.997 Mean :11.77
3rd Qu.: 0.000 3rd Qu.:22.23 3rd Qu.:11.300 3rd Qu.:16.50 3rd Qu.:22.62 3rd Qu.:4.900 3rd Qu.:13.72
Max. :31.800 Max. :33.80 Max. :20.600 Max. :26.20 Max. :33.40 Max. :9.300 Max. :22.00
Pp Tb TTbeta Tbb TTbroken TbF TTfick
Min. :10.02 Min. :1 Min. : 0.128 Min. :1 Min. : 1.841 Min. :1 Min. : 2.222
1st Qu.:12.21 1st Qu.:1 1st Qu.: 2.247 1st Qu.:1 1st Qu.: 6.822 1st Qu.:1 1st Qu.: 8.172
Median :14.27 Median :1 Median : 4.118 Median :1 Median : 9.015 Median :1 Median :10.646
Mean :13.93 Mean :1 Mean : 5.812 Mean :1 Mean : 9.535 Mean :1 Mean :10.878
3rd Qu.:15.82 3rd Qu.:1 3rd Qu.: 8.085 3rd Qu.:1 3rd Qu.:12.040 3rd Qu.:1 3rd Qu.:13.588
Max. :16.65 Max. :1 Max. :23.343 Max. :1 Max. :20.258 Max. :1 Max. :20.813
ExpName Experiment.y Water.y Defoliation.y SowingDate.y FD.y
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Lincoln1997: 68 dry: 0 HH: 0 No :175 FD10: 0
Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Lincoln2000:110 irr:408 LL:408 no :108 FD2 : 0
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Lincoln2001: 15 LS: 0 SD1 : 67 FD5 :408
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Lincoln2002:107 SL: 0 SD2 : 25
Iversen_8WaterirrGs_5Rt_1 : 10 Lincoln2010: 0 SS: 0 SD3 : 18
Iversen_91DefoliationLLGs_2Rt_1 : 10 Lincoln2015:108 SD4 : 15
(Other) :340 (Other): 0
GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean
Gs_1:160 Rt_1 :112 Min. : 0.2834 Min. : 4.022 Min. : 3.333 Min. :10.25 Min. : 7.024
Gs_2:140 Rt_2 : 80 1st Qu.: 60.8023 1st Qu.: 165.210 1st Qu.: 142.690 1st Qu.:11.99 1st Qu.:10.974
Gs_3: 54 Rt_3 : 59 Median :123.4401 Median : 299.224 Median : 258.979 Median :14.48 Median :13.963
Gs_4: 13 Rt_4 : 50 Mean :157.2934 Mean : 326.606 Mean : 283.618 Mean :13.84 Mean :13.226
Gs_5: 41 Rt_5 : 46 3rd Qu.:218.2569 3rd Qu.: 453.159 3rd Qu.: 395.238 3rd Qu.:15.99 3rd Qu.:15.771
Gs_6: 0 Rt_6 : 42 Max. :722.6708 Max. :1186.654 Max. :1051.876 Max. :16.55 Max. :19.327
(Other): 19
GrowthRotation Predicted
11 : 60 Min. : 0.0
12 : 38 1st Qu.:103.4
21 : 24 Median :222.0
26 : 24 Mean :266.7
13 : 23 3rd Qu.:403.0
22 : 23 Max. :858.6
(Other):216 str(mergedf)'data.frame': 408 obs. of 54 variables:
$ Clock.Today : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
$ Variable : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
$ ExpUnitCode : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
$ Collection : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment.x : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Water.x : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.x : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.x : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.x: int 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.x : int 2 2 2 2 2 3 3 3 4 4 ...
$ StartDate : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
$ MidDate : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
$ FinishDate : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
$ Interval : int 16 21 27 34 41 19 28 33 22 30 ...
$ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 62.7 146.7 320.7 436.7 564.3 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
$ Clock.Today1 : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
$ year : int 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
$ day : int 296 301 307 314 321 343 352 357 15 23 ...
$ rain : num 0 0.7 0 0 0 0 0 0 0 0 ...
$ maxt : num 15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
$ mint : num 8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
$ mean : num 12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
$ radn : num 20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
$ wind : num 4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
$ vp : num 10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
$ Pp : num 14.6 14.9 15.2 15.5 15.8 ...
$ Tb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbeta : num 2.652 8.148 9.211 0.944 4.723 ...
$ Tbb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbroken : num 7.87 11.44 11.84 5.3 9.66 ...
$ TbF : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTfick : num 9.49 12.74 13.02 6.4 11.33 ...
$ ExpName : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
$ Experiment.y : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water.y : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.y : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.y : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.y : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
$ Tt_beta_sum : num 69.7 96.2 141.7 172.5 217.7 ...
$ Tt_fick_sum : num 166 220 295 363 447 ...
$ Tt_broken_sum : num 142 189 256 315 388 ...
$ Ppm : num 14.9 14.9 14.9 14.9 14.9 ...
$ Tmean : num 13.2 13.2 13.2 13.2 13.2 ...
$ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
$ Predicted : num 187 254 355 443 554 ...mergedf %>%
dplyr::filter(Variable== "Height") %>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=2)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Plant height")+
facet_wrap(~Collection, ncol = 4)+
theme(legend.title=element_blank())+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
2002-2004
mergedfsummary(mergedf) Clock.Today Name Variable ExpUnitCode
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5 :108 Height :408 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14
1st Qu.:2001-02-20 Iversen_91DefoliationLL :107 Branch : 0 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12
Median :2002-10-25 Iversen_8Waterirr : 68 Fraction : 0 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11
Mean :2005-09-17 Iversen_9SowingDateSD1Waterirr: 67 HardStemWt: 0 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11
3rd Qu.:2015-02-09 Iversen_9SowingDateSD2Waterirr: 25 LAI : 0 Iversen_8WaterirrGs_5Rt_1 : 10
Max. :2018-01-15 Iversen_9SowingDateSD3Waterirr: 18 LeafWt : 0 Iversen_91DefoliationLLGs_2Rt_1 : 10
(Other) : 15 (Other) : 0 (Other) :340
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x GrowthSeason.x Rotation.x
1997_2001: 68 Lincoln2000:131 dry: 0 HH: 0 No :175 FD10: 0 Min. :1.000 Min. :1.000
2000_2002:125 Lincoln2015:108 irr:408 LL:408 no :108 FD2 : 0 1st Qu.:1.000 1st Qu.:1.000
2002_2004:107 Lincoln2003: 54 SD1 : 67 FD5 :408 Median :2.000 Median :3.000
2010_2012: 0 Lincoln2001: 35 SD2 : 25 Mean :2.105 Mean :3.098
2014_2018:108 Lincoln2004: 31 SD3 : 18 3rd Qu.:3.000 3rd Qu.:5.000
2014_2019: 0 Lincoln2002: 22 SD4 : 15 Max. :5.000 Max. :7.000
(Other) : 27 (Other): 0
StartDate MidDate FinishDate Interval VariableUnits Time
Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19 Min. : 0.00 % : 0 12:00:00:408
1st Qu.:2001-01-24 1st Qu.:2001-02-13 1st Qu.:2001-03-23 1st Qu.: 14.75 cm :233
Median :2002-10-06 Median :2002-10-26 Median :2002-11-16 Median : 28.00 fractio0l: 0
Mean :2005-08-16 Mean :2005-09-14 Mean :2005-10-14 Mean : 32.18 Fraction : 0
3rd Qu.:2015-01-30 3rd Qu.:2015-02-19 3rd Qu.:2015-03-11 3rd Qu.: 41.00 kg/ha : 0
Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :116.00 m2/m2 : 0
mm :175
Observed StdDEV GrowthSeason1 Rotation1 Clock.Today1 year day
Min. : 0.00 Min. : 0.000 Gs_1:160 Rt_1:112 Min. :1997-10-23 12:00:00 Min. :1997 Min. : 1.0
1st Qu.: 62.12 1st Qu.: 0.000 Gs_2:140 Rt_2: 80 1st Qu.:2001-02-20 12:00:00 1st Qu.:2001 1st Qu.: 63.0
Median :185.00 Median : 3.107 Gs_3: 54 Rt_3: 59 Median :2002-10-25 12:00:00 Median :2002 Median :138.5
Mean :219.48 Mean :12.001 Gs_4: 13 Rt_4: 50 Mean :2005-09-17 22:25:44 Mean :2005 Mean :170.4
3rd Qu.:360.50 3rd Qu.:16.955 Gs_5: 41 Rt_5: 46 3rd Qu.:2015-02-10 06:00:00 3rd Qu.:2015 3rd Qu.:292.0
Max. :798.00 Max. :91.520 Gs_6: 0 Rt_6: 42 Max. :2018-01-15 12:00:00 Max. :2018 Max. :365.0
NA's :229 Rt_7: 19
rain maxt mint mean radn wind vp
Min. : 0.000 Min. : 7.90 Min. :-4.900 Min. : 2.55 Min. : 1.50 Min. :0.700 Min. : 5.10
1st Qu.: 0.000 1st Qu.:15.32 1st Qu.: 4.375 1st Qu.:10.40 1st Qu.: 9.70 1st Qu.:2.900 1st Qu.:10.00
Median : 0.000 Median :19.00 Median : 8.700 Median :13.20 Median :16.14 Median :3.900 Median :11.40
Mean : 0.876 Mean :19.17 Mean : 7.853 Mean :13.49 Mean :16.73 Mean :3.997 Mean :11.77
3rd Qu.: 0.000 3rd Qu.:22.23 3rd Qu.:11.300 3rd Qu.:16.50 3rd Qu.:22.62 3rd Qu.:4.900 3rd Qu.:13.72
Max. :31.800 Max. :33.80 Max. :20.600 Max. :26.20 Max. :33.40 Max. :9.300 Max. :22.00
Pp Tb TTbeta Tbb TTbroken TbF TTfick
Min. :10.02 Min. :1 Min. : 0.128 Min. :1 Min. : 1.841 Min. :1 Min. : 2.222
1st Qu.:12.21 1st Qu.:1 1st Qu.: 2.247 1st Qu.:1 1st Qu.: 6.822 1st Qu.:1 1st Qu.: 8.172
Median :14.27 Median :1 Median : 4.118 Median :1 Median : 9.015 Median :1 Median :10.646
Mean :13.93 Mean :1 Mean : 5.812 Mean :1 Mean : 9.535 Mean :1 Mean :10.878
3rd Qu.:15.82 3rd Qu.:1 3rd Qu.: 8.085 3rd Qu.:1 3rd Qu.:12.040 3rd Qu.:1 3rd Qu.:13.588
Max. :16.65 Max. :1 Max. :23.343 Max. :1 Max. :20.258 Max. :1 Max. :20.813
ExpName Experiment.y Water.y Defoliation.y SowingDate.y FD.y
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Lincoln1997: 68 dry: 0 HH: 0 No :175 FD10: 0
Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Lincoln2000:110 irr:408 LL:408 no :108 FD2 : 0
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Lincoln2001: 15 LS: 0 SD1 : 67 FD5 :408
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Lincoln2002:107 SL: 0 SD2 : 25
Iversen_8WaterirrGs_5Rt_1 : 10 Lincoln2010: 0 SS: 0 SD3 : 18
Iversen_91DefoliationLLGs_2Rt_1 : 10 Lincoln2015:108 SD4 : 15
(Other) :340 (Other): 0
GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean
Gs_1:160 Rt_1 :112 Min. : 0.2834 Min. : 4.022 Min. : 3.333 Min. :10.25 Min. : 7.024
Gs_2:140 Rt_2 : 80 1st Qu.: 60.8023 1st Qu.: 165.210 1st Qu.: 142.690 1st Qu.:11.99 1st Qu.:10.974
Gs_3: 54 Rt_3 : 59 Median :123.4401 Median : 299.224 Median : 258.979 Median :14.48 Median :13.963
Gs_4: 13 Rt_4 : 50 Mean :157.2934 Mean : 326.606 Mean : 283.618 Mean :13.84 Mean :13.226
Gs_5: 41 Rt_5 : 46 3rd Qu.:218.2569 3rd Qu.: 453.159 3rd Qu.: 395.238 3rd Qu.:15.99 3rd Qu.:15.771
Gs_6: 0 Rt_6 : 42 Max. :722.6708 Max. :1186.654 Max. :1051.876 Max. :16.55 Max. :19.327
(Other): 19
GrowthRotation Predicted
11 : 60 Min. : 0.0
12 : 38 1st Qu.:103.4
21 : 24 Median :222.0
26 : 24 Mean :266.7
13 : 23 3rd Qu.:403.0
22 : 23 Max. :858.6
(Other):216 str(mergedf)'data.frame': 408 obs. of 54 variables:
$ Clock.Today : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
$ Variable : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
$ ExpUnitCode : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
$ Collection : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment.x : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Water.x : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.x : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.x : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.x: int 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.x : int 2 2 2 2 2 3 3 3 4 4 ...
$ StartDate : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
$ MidDate : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
$ FinishDate : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
$ Interval : int 16 21 27 34 41 19 28 33 22 30 ...
$ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 62.7 146.7 320.7 436.7 564.3 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
$ Clock.Today1 : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
$ year : int 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
$ day : int 296 301 307 314 321 343 352 357 15 23 ...
$ rain : num 0 0.7 0 0 0 0 0 0 0 0 ...
$ maxt : num 15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
$ mint : num 8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
$ mean : num 12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
$ radn : num 20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
$ wind : num 4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
$ vp : num 10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
$ Pp : num 14.6 14.9 15.2 15.5 15.8 ...
$ Tb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbeta : num 2.652 8.148 9.211 0.944 4.723 ...
$ Tbb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbroken : num 7.87 11.44 11.84 5.3 9.66 ...
$ TbF : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTfick : num 9.49 12.74 13.02 6.4 11.33 ...
$ ExpName : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
$ Experiment.y : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water.y : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.y : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.y : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.y : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
$ Tt_beta_sum : num 69.7 96.2 141.7 172.5 217.7 ...
$ Tt_fick_sum : num 166 220 295 363 447 ...
$ Tt_broken_sum : num 142 189 256 315 388 ...
$ Ppm : num 14.9 14.9 14.9 14.9 14.9 ...
$ Tmean : num 13.2 13.2 13.2 13.2 13.2 ...
$ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
$ Predicted : num 187 254 355 443 554 ...mergedf %>%
dplyr::filter(Collection=="2002_2004")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Plant height")+
facet_grid(GrowthSeason.x~Rotation.x)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
2000-2002
mergedfsummary(mergedf) Clock.Today Name Variable ExpUnitCode
Min. :1997-10-23 Iversen_121DefoliationLLFDFD5 :108 Height :408 Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14
1st Qu.:2001-02-20 Iversen_91DefoliationLL :107 Branch : 0 Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12
Median :2002-10-25 Iversen_8Waterirr : 68 Fraction : 0 Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11
Mean :2005-09-17 Iversen_9SowingDateSD1Waterirr: 67 HardStemWt: 0 Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11
3rd Qu.:2015-02-09 Iversen_9SowingDateSD2Waterirr: 25 LAI : 0 Iversen_8WaterirrGs_5Rt_1 : 10
Max. :2018-01-15 Iversen_9SowingDateSD3Waterirr: 18 LeafWt : 0 Iversen_91DefoliationLLGs_2Rt_1 : 10
(Other) : 15 (Other) : 0 (Other) :340
Collection Experiment.x Water.x Defoliation.x SowingDate.x FD.x GrowthSeason.x Rotation.x
1997_2001: 68 Lincoln2000:131 dry: 0 HH: 0 No :175 FD10: 0 Min. :1.000 Min. :1.000
2000_2002:125 Lincoln2015:108 irr:408 LL:408 no :108 FD2 : 0 1st Qu.:1.000 1st Qu.:1.000
2002_2004:107 Lincoln2003: 54 SD1 : 67 FD5 :408 Median :2.000 Median :3.000
2010_2012: 0 Lincoln2001: 35 SD2 : 25 Mean :2.105 Mean :3.098
2014_2018:108 Lincoln2004: 31 SD3 : 18 3rd Qu.:3.000 3rd Qu.:5.000
2014_2019: 0 Lincoln2002: 22 SD4 : 15 Max. :5.000 Max. :7.000
(Other) : 27 (Other): 0
StartDate MidDate FinishDate Interval VariableUnits Time
Min. :1997-10-07 Min. :1997-10-28 Min. :1997-11-19 Min. : 0.00 % : 0 12:00:00:408
1st Qu.:2001-01-24 1st Qu.:2001-02-13 1st Qu.:2001-03-23 1st Qu.: 14.75 cm :233
Median :2002-10-06 Median :2002-10-26 Median :2002-11-16 Median : 28.00 fractio0l: 0
Mean :2005-08-16 Mean :2005-09-14 Mean :2005-10-14 Mean : 32.18 Fraction : 0
3rd Qu.:2015-01-30 3rd Qu.:2015-02-19 3rd Qu.:2015-03-11 3rd Qu.: 41.00 kg/ha : 0
Max. :2017-12-04 Max. :2017-12-25 Max. :2018-01-15 Max. :116.00 m2/m2 : 0
mm :175
Observed StdDEV GrowthSeason1 Rotation1 Clock.Today1 year day
Min. : 0.00 Min. : 0.000 Gs_1:160 Rt_1:112 Min. :1997-10-23 12:00:00 Min. :1997 Min. : 1.0
1st Qu.: 62.12 1st Qu.: 0.000 Gs_2:140 Rt_2: 80 1st Qu.:2001-02-20 12:00:00 1st Qu.:2001 1st Qu.: 63.0
Median :185.00 Median : 3.107 Gs_3: 54 Rt_3: 59 Median :2002-10-25 12:00:00 Median :2002 Median :138.5
Mean :219.48 Mean :12.001 Gs_4: 13 Rt_4: 50 Mean :2005-09-17 22:25:44 Mean :2005 Mean :170.4
3rd Qu.:360.50 3rd Qu.:16.955 Gs_5: 41 Rt_5: 46 3rd Qu.:2015-02-10 06:00:00 3rd Qu.:2015 3rd Qu.:292.0
Max. :798.00 Max. :91.520 Gs_6: 0 Rt_6: 42 Max. :2018-01-15 12:00:00 Max. :2018 Max. :365.0
NA's :229 Rt_7: 19
rain maxt mint mean radn wind vp
Min. : 0.000 Min. : 7.90 Min. :-4.900 Min. : 2.55 Min. : 1.50 Min. :0.700 Min. : 5.10
1st Qu.: 0.000 1st Qu.:15.32 1st Qu.: 4.375 1st Qu.:10.40 1st Qu.: 9.70 1st Qu.:2.900 1st Qu.:10.00
Median : 0.000 Median :19.00 Median : 8.700 Median :13.20 Median :16.14 Median :3.900 Median :11.40
Mean : 0.876 Mean :19.17 Mean : 7.853 Mean :13.49 Mean :16.73 Mean :3.997 Mean :11.77
3rd Qu.: 0.000 3rd Qu.:22.23 3rd Qu.:11.300 3rd Qu.:16.50 3rd Qu.:22.62 3rd Qu.:4.900 3rd Qu.:13.72
Max. :31.800 Max. :33.80 Max. :20.600 Max. :26.20 Max. :33.40 Max. :9.300 Max. :22.00
Pp Tb TTbeta Tbb TTbroken TbF TTfick
Min. :10.02 Min. :1 Min. : 0.128 Min. :1 Min. : 1.841 Min. :1 Min. : 2.222
1st Qu.:12.21 1st Qu.:1 1st Qu.: 2.247 1st Qu.:1 1st Qu.: 6.822 1st Qu.:1 1st Qu.: 8.172
Median :14.27 Median :1 Median : 4.118 Median :1 Median : 9.015 Median :1 Median :10.646
Mean :13.93 Mean :1 Mean : 5.812 Mean :1 Mean : 9.535 Mean :1 Mean :10.878
3rd Qu.:15.82 3rd Qu.:1 3rd Qu.: 8.085 3rd Qu.:1 3rd Qu.:12.040 3rd Qu.:1 3rd Qu.:13.588
Max. :16.65 Max. :1 Max. :23.343 Max. :1 Max. :20.258 Max. :1 Max. :20.813
ExpName Experiment.y Water.y Defoliation.y SowingDate.y FD.y
Iversen_9SowingDateSD2WaterirrGs_1Rt_1: 14 Lincoln1997: 68 dry: 0 HH: 0 No :175 FD10: 0
Iversen_9SowingDateSD1WaterirrGs_1Rt_1: 12 Lincoln2000:110 irr:408 LL:408 no :108 FD2 : 0
Iversen_9SowingDateSD3WaterirrGs_1Rt_1: 11 Lincoln2001: 15 LS: 0 SD1 : 67 FD5 :408
Iversen_9SowingDateSD4WaterirrGs_1Rt_1: 11 Lincoln2002:107 SL: 0 SD2 : 25
Iversen_8WaterirrGs_5Rt_1 : 10 Lincoln2010: 0 SS: 0 SD3 : 18
Iversen_91DefoliationLLGs_2Rt_1 : 10 Lincoln2015:108 SD4 : 15
(Other) :340 (Other): 0
GrowthSeason.y Rotation.y Tt_beta_sum Tt_fick_sum Tt_broken_sum Ppm Tmean
Gs_1:160 Rt_1 :112 Min. : 0.2834 Min. : 4.022 Min. : 3.333 Min. :10.25 Min. : 7.024
Gs_2:140 Rt_2 : 80 1st Qu.: 60.8023 1st Qu.: 165.210 1st Qu.: 142.690 1st Qu.:11.99 1st Qu.:10.974
Gs_3: 54 Rt_3 : 59 Median :123.4401 Median : 299.224 Median : 258.979 Median :14.48 Median :13.963
Gs_4: 13 Rt_4 : 50 Mean :157.2934 Mean : 326.606 Mean : 283.618 Mean :13.84 Mean :13.226
Gs_5: 41 Rt_5 : 46 3rd Qu.:218.2569 3rd Qu.: 453.159 3rd Qu.: 395.238 3rd Qu.:15.99 3rd Qu.:15.771
Gs_6: 0 Rt_6 : 42 Max. :722.6708 Max. :1186.654 Max. :1051.876 Max. :16.55 Max. :19.327
(Other): 19
GrowthRotation Predicted
11 : 60 Min. : 0.0
12 : 38 1st Qu.:103.4
21 : 24 Median :222.0
26 : 24 Mean :266.7
13 : 23 3rd Qu.:403.0
22 : 23 Max. :858.6
(Other):216 str(mergedf)'data.frame': 408 obs. of 54 variables:
$ Clock.Today : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
$ Variable : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
$ ExpUnitCode : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
$ Collection : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment.x : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Water.x : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.x : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.x : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.x: int 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.x : int 2 2 2 2 2 3 3 3 4 4 ...
$ StartDate : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
$ MidDate : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
$ FinishDate : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
$ Interval : int 16 21 27 34 41 19 28 33 22 30 ...
$ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 62.7 146.7 320.7 436.7 564.3 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
$ Clock.Today1 : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
$ year : int 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
$ day : int 296 301 307 314 321 343 352 357 15 23 ...
$ rain : num 0 0.7 0 0 0 0 0 0 0 0 ...
$ maxt : num 15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
$ mint : num 8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
$ mean : num 12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
$ radn : num 20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
$ wind : num 4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
$ vp : num 10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
$ Pp : num 14.6 14.9 15.2 15.5 15.8 ...
$ Tb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbeta : num 2.652 8.148 9.211 0.944 4.723 ...
$ Tbb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbroken : num 7.87 11.44 11.84 5.3 9.66 ...
$ TbF : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTfick : num 9.49 12.74 13.02 6.4 11.33 ...
$ ExpName : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
$ Experiment.y : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water.y : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.y : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.y : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.y : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
$ Tt_beta_sum : num 69.7 96.2 141.7 172.5 217.7 ...
$ Tt_fick_sum : num 166 220 295 363 447 ...
$ Tt_broken_sum : num 142 189 256 315 388 ...
$ Ppm : num 14.9 14.9 14.9 14.9 14.9 ...
$ Tmean : num 13.2 13.2 13.2 13.2 13.2 ...
$ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
$ Predicted : num 187 254 355 443 554 ...mergedf %>%
dplyr::filter(Collection=="2000_2002")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw()+
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1)+
ggtitle("Height")+
facet_grid(GrowthSeason.x~Rotation.x)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
mergedf %>%
dplyr::filter(Collection=="1997_2001")%>%
ggplot(aes(x=Observed, y= Predicted,
colour= factor(Name))) +
geom_point(size=3)+theme_bw() +
geom_smooth(method = "lm", se = TRUE, linetype = 1, colour="darkgrey") +
geom_abline(intercept = 0, slope = 1) +
coord_fixed(ratio = 1) +
ggtitle("Plant height") +
facet_grid(GrowthSeason.x~Rotation.x)+
theme(legend.title=element_blank(),legend.position = "blank")+xlab("Observed")+ylab("Predicted")+
theme(axis.title.x=element_text(face="bold",colour="black",size = 12))+
theme(axis.title.y=element_text(face="bold",colour="black",size = 12))
RMSE
str(mergedf)'data.frame': 408 obs. of 54 variables:
$ Clock.Today : Date, format: "1997-10-23" "1997-10-28" "1997-11-03" "1997-11-10" ...
$ Name : Factor w/ 25 levels "Iversen_121DefoliationHHFDFD5",..: 16 16 16 16 16 16 16 16 16 16 ...
$ Variable : Factor w/ 18 levels "Branch","Fraction",..: 4 4 4 4 4 4 4 4 4 4 ...
$ ExpUnitCode : Factor w/ 263 levels "Iversen_121DefoliationHHFDFD5Gs_1Rt_2",..: 152 152 152 152 152 153 153 153 154 154 ...
$ Collection : Factor w/ 6 levels "1997_2001","2000_2002",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Experiment.x : Factor w/ 11 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 2 2 ...
$ Water.x : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.x : Factor w/ 2 levels "HH","LL": 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.x : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.x : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.x: int 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.x : int 2 2 2 2 2 3 3 3 4 4 ...
$ StartDate : Date, format: "1997-10-07" "1997-10-07" "1997-10-07" "1997-10-07" ...
$ MidDate : Date, format: "1997-10-28" "1997-10-28" "1997-10-28" "1997-10-28" ...
$ FinishDate : Date, format: "1997-11-19" "1997-11-19" "1997-11-19" "1997-11-19" ...
$ Interval : int 16 21 27 34 41 19 28 33 22 30 ...
$ VariableUnits : Factor w/ 7 levels "%","cm","fractio0l",..: 7 7 7 7 7 7 7 7 7 7 ...
$ Time : Factor w/ 1 level "12:00:00": 1 1 1 1 1 1 1 1 1 1 ...
$ Observed : num 62.7 146.7 320.7 436.7 564.3 ...
$ StdDEV : num 0 0 0 0 0 0 0 0 0 0 ...
$ GrowthSeason1 : Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation1 : Factor w/ 7 levels "Rt_1","Rt_2",..: 2 2 2 2 2 3 3 3 4 4 ...
$ Clock.Today1 : POSIXct, format: "1997-10-23 12:00:00" "1997-10-28 12:00:00" "1997-11-03 12:00:00" "1997-11-10 12:00:00" ...
$ year : int 1997 1997 1997 1997 1997 1997 1997 1997 1998 1998 ...
$ day : int 296 301 307 314 321 343 352 357 15 23 ...
$ rain : num 0 0.7 0 0 0 0 0 0 0 0 ...
$ maxt : num 15.1 24.3 26 12 18.5 19.4 28 31.9 31.4 25.1 ...
$ mint : num 8.8 6.9 5.8 4.7 9.6 8 17 14.1 14.2 15.6 ...
$ mean : num 12 15.6 15.9 8.4 14.1 13.7 22.5 23 22.8 20.4 ...
$ radn : num 20.2 15.6 26.2 26 22.5 30.7 21.6 19.7 22.1 16 ...
$ wind : num 4.3 2.9 5.6 5.7 5.8 4.2 6.1 4.9 7.8 3.9 ...
$ vp : num 10.8 13.1 11.5 7.9 10.6 12.3 12.7 16.1 14.2 17.4 ...
$ Pp : num 14.6 14.9 15.2 15.5 15.8 ...
$ Tb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbeta : num 2.652 8.148 9.211 0.944 4.723 ...
$ Tbb : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTbroken : num 7.87 11.44 11.84 5.3 9.66 ...
$ TbF : int 1 1 1 1 1 1 1 1 1 1 ...
$ TTfick : num 9.49 12.74 13.02 6.4 11.33 ...
$ ExpName : Factor w/ 338 levels "Iversen_121DefoliationHHFDFD10Gs_1Rt_2",..: 171 171 171 171 171 172 172 172 173 173 ...
$ Experiment.y : Factor w/ 6 levels "Lincoln1997",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Water.y : Factor w/ 2 levels "dry","irr": 2 2 2 2 2 2 2 2 2 2 ...
$ Defoliation.y : Factor w/ 5 levels "HH","LL","LS",..: 2 2 2 2 2 2 2 2 2 2 ...
$ SowingDate.y : Factor w/ 12 levels "no","No","SD1",..: 2 2 2 2 2 2 2 2 2 2 ...
$ FD.y : Factor w/ 3 levels "FD10","FD2","FD5": 3 3 3 3 3 3 3 3 3 3 ...
$ GrowthSeason.y: Factor w/ 6 levels "Gs_1","Gs_2",..: 2 2 2 2 2 2 2 2 2 2 ...
$ Rotation.y : Factor w/ 10 levels "Rt_1","Rt_10",..: 3 3 3 3 3 4 4 4 5 5 ...
$ Tt_beta_sum : num 69.7 96.2 141.7 172.5 217.7 ...
$ Tt_fick_sum : num 166 220 295 363 447 ...
$ Tt_broken_sum : num 142 189 256 315 388 ...
$ Ppm : num 14.9 14.9 14.9 14.9 14.9 ...
$ Tmean : num 13.2 13.2 13.2 13.2 13.2 ...
$ GrowthRotation: Factor w/ 36 levels "11","12","13",..: 9 9 9 9 9 10 10 10 11 11 ...
$ Predicted : num 187 254 355 443 554 ...mergedf %>%
group_by(Name) %>%
summarise(
n = n(),
r2 = gauchStats(Predicted,Observed)[5],
# rmse = round(rmse(Predicted,Observed),0),
r_rmse = round(rmse(Predicted,Observed)/mean(Observed)*100,1),
nse = round(NSE(Predicted,Observed),1),
sb = gauchStats(Predicted,Observed)[1],
nu = gauchStats(Predicted,Observed)[2],
lc = gauchStats(Predicted,Observed)[3]
) # %>%
# group_by(Variable,Name) %>%
# summarise_each(funs(mean))
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